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5,753
legislator
bird:train.json:4775
Give the YouTube ID of the channel 'RepWassermanSchultz.'
SELECT youtube_id FROM `social-media` WHERE youtube = 'RepWassermanSchultz'
[ "Give", "the", "YouTube", "ID", "of", "the", "channel", "'", "RepWassermanSchultz", ".", "'" ]
[ { "id": 3, "type": "value", "value": "RepWassermanSchultz" }, { "id": 0, "type": "table", "value": "social-media" }, { "id": 1, "type": "column", "value": "youtube_id" }, { "id": 2, "type": "column", "value": "youtube" } ]
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[ "O", "O", "B-COLUMN", "B-COLUMN", "O", "O", "O", "O", "B-VALUE", "O", "O" ]
15,219
sales
bird:train.json:5415
List the full name of the customer who purchased the most quantity of products.
SELECT T1.FirstName, T1.LastName FROM Customers AS T1 INNER JOIN Sales AS T2 ON T1.CustomerID = T2.CustomerID ORDER BY T2.Quantity DESC LIMIT 1
[ "List", "the", "full", "name", "of", "the", "customer", "who", "purchased", "the", "most", "quantity", "of", "products", "." ]
[ { "id": 5, "type": "column", "value": "customerid" }, { "id": 0, "type": "column", "value": "firstname" }, { "id": 2, "type": "table", "value": "customers" }, { "id": 1, "type": "column", "value": "lastname" }, { "id": 4, "type": "column", "value": "quantity" }, { "id": 3, "type": "table", "value": "sales" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O", "O", "O" ]
4,471
customers_and_addresses
spider:train_spider.json:6059
What is the average amount of items ordered in each order?
SELECT avg(order_quantity) FROM order_items
[ "What", "is", "the", "average", "amount", "of", "items", "ordered", "in", "each", "order", "?" ]
[ { "id": 1, "type": "column", "value": "order_quantity" }, { "id": 0, "type": "table", "value": "order_items" } ]
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[ "O", "O", "O", "O", "O", "O", "B-TABLE", "I-TABLE", "O", "O", "B-COLUMN", "O" ]
5,507
tracking_share_transactions
spider:train_spider.json:5859
How many distinct transaction types are used in the transactions?
SELECT COUNT(DISTINCT transaction_type_code) FROM TRANSACTIONS
[ "How", "many", "distinct", "transaction", "types", "are", "used", "in", "the", "transactions", "?" ]
[ { "id": 1, "type": "column", "value": "transaction_type_code" }, { "id": 0, "type": "table", "value": "transactions" } ]
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[ "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O", "B-TABLE", "O" ]
14,485
movie_platform
bird:train.json:13
For movie titled 'Welcome to the Dollhouse', how many percentage of the ratings were rated with highest score.
SELECT CAST(SUM(CASE WHEN T2.rating_score = 5 THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*) FROM movies AS T1 INNER JOIN ratings AS T2 ON T1.movie_id = T2.movie_id WHERE T1.movie_title = 'Welcome to the Dollhouse'
[ "For", "movie", "titled", "'", "Welcome", "to", "the", "Dollhouse", "'", ",", "how", "many", "percentage", "of", "the", "ratings", "were", "rated", "with", "highest", "score", "." ]
[ { "id": 3, "type": "value", "value": "Welcome to the Dollhouse" }, { "id": 8, "type": "column", "value": "rating_score" }, { "id": 2, "type": "column", "value": "movie_title" }, { "id": 4, "type": "column", "value": "movie_id" }, { "id": 1, "type": "table", "value": "ratings" }, { "id": 0, "type": "table", "value": "movies" }, { "id": 5, "type": "value", "value": "100" }, { "id": 6, "type": "value", "value": "0" }, { "id": 7, "type": "value", "value": "1" }, { "id": 9, "type": "value", "value": "5" } ]
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[ "O", "B-TABLE", "B-COLUMN", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "B-COLUMN", "O" ]
6,455
hockey
bird:train.json:7689
Did legendsID "P194502" personally attend his Hall of Fame dedication?
SELECT IIF(T1.note = 'posthumous', 'YES', 'NO') FROM AwardsMisc AS T1 RIGHT JOIN Master AS T2 ON T1.ID = T2.playerID WHERE T2.legendsID = 'P194502'
[ "Did", "legendsID", "\"", "P194502", "\"", "personally", "attend", "his", "Hall", "of", "Fame", "dedication", "?" ]
[ { "id": 0, "type": "table", "value": "awardsmisc" }, { "id": 9, "type": "value", "value": "posthumous" }, { "id": 2, "type": "column", "value": "legendsid" }, { "id": 7, "type": "column", "value": "playerid" }, { "id": 3, "type": "value", "value": "P194502" }, { "id": 1, "type": "table", "value": "master" }, { "id": 8, "type": "column", "value": "note" }, { "id": 4, "type": "value", "value": "YES" }, { "id": 5, "type": "value", "value": "NO" }, { "id": 6, "type": "column", "value": "id" } ]
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[ "B-COLUMN", "B-COLUMN", "O", "B-VALUE", "O", "B-VALUE", "B-TABLE", "O", "O", "O", "O", "O", "O" ]
9,883
manufacturer
spider:train_spider.json:3389
How many furniture components are there in total?
SELECT sum(num_of_component) FROM furniture
[ "How", "many", "furniture", "components", "are", "there", "in", "total", "?" ]
[ { "id": 1, "type": "column", "value": "num_of_component" }, { "id": 0, "type": "table", "value": "furniture" } ]
[ { "entity_id": 0, "token_idxs": [ 2 ] }, { "entity_id": 1, "token_idxs": [ 3 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-TABLE", "B-COLUMN", "O", "O", "O", "O", "O" ]
10,926
works_cycles
bird:train.json:7148
What is the minimum shipping charge for "OVERSEAS - DELUXE"?
SELECT ShipBase FROM ShipMethod WHERE Name = 'OVERSEAS - DELUXE'
[ "What", "is", "the", "minimum", "shipping", "charge", "for", "\"", "OVERSEAS", "-", "DELUXE", "\"", "?" ]
[ { "id": 3, "type": "value", "value": "OVERSEAS - DELUXE" }, { "id": 0, "type": "table", "value": "shipmethod" }, { "id": 1, "type": "column", "value": "shipbase" }, { "id": 2, "type": "column", "value": "name" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "O", "O" ]
3,295
car_racing
bird:test.json:1629
List the names of teams that do not have any drivers.
SELECT Team FROM team WHERE Team_ID NOT IN (SELECT Team_ID FROM team_driver)
[ "List", "the", "names", "of", "teams", "that", "do", "not", "have", "any", "drivers", "." ]
[ { "id": 3, "type": "table", "value": "team_driver" }, { "id": 2, "type": "column", "value": "team_id" }, { "id": 0, "type": "table", "value": "team" }, { "id": 1, "type": "column", "value": "team" } ]
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[ "O", "O", "O", "O", "B-TABLE", "B-COLUMN", "O", "O", "O", "B-TABLE", "B-COLUMN", "O" ]
16,027
gas_company
spider:train_spider.json:2028
What are the names of the managers for gas stations that are operated by the ExxonMobil company?
SELECT T3.manager_name FROM station_company AS T1 JOIN company AS T2 ON T1.company_id = T2.company_id JOIN gas_station AS T3 ON T1.station_id = T3.station_id WHERE T2.company = 'ExxonMobil'
[ "What", "are", "the", "names", "of", "the", "managers", "for", "gas", "stations", "that", "are", "operated", "by", "the", "ExxonMobil", "company", "?" ]
[ { "id": 4, "type": "table", "value": "station_company" }, { "id": 0, "type": "column", "value": "manager_name" }, { "id": 1, "type": "table", "value": "gas_station" }, { "id": 3, "type": "value", "value": "ExxonMobil" }, { "id": 6, "type": "column", "value": "station_id" }, { "id": 7, "type": "column", "value": "company_id" }, { "id": 2, "type": "column", "value": "company" }, { "id": 5, "type": "table", "value": "company" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "O", "B-TABLE", "B-COLUMN", "O", "O", "O", "O", "O", "B-VALUE", "B-TABLE", "O" ]
11,769
chicago_crime
bird:train.json:8684
Find the ward office's address and phone number of the ward where the most crimes without arrest occurred.
SELECT T2.ward_office_address, T2.ward_office_phone FROM Crime AS T1 INNER JOIN Ward AS T2 ON T2.ward_no = T1.ward_no WHERE T1.arrest = 'FALSE' GROUP BY T2.ward_office_address, T2.ward_office_phone ORDER BY COUNT(T1.arrest) DESC LIMIT 1
[ "Find", "the", "ward", "office", "'s", "address", "and", "phone", "number", "of", "the", "ward", "where", "the", "most", "crimes", "without", "arrest", "occurred", "." ]
[ { "id": 0, "type": "column", "value": "ward_office_address" }, { "id": 1, "type": "column", "value": "ward_office_phone" }, { "id": 6, "type": "column", "value": "ward_no" }, { "id": 4, "type": "column", "value": "arrest" }, { "id": 2, "type": "table", "value": "crime" }, { "id": 5, "type": "value", "value": "FALSE" }, { "id": 3, "type": "table", "value": "ward" } ]
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[ "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "I-COLUMN", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "B-COLUMN", "O", "O" ]
9,977
law_episode
bird:train.json:1340
What is the episode rating with the most award won?
SELECT T1.rating FROM Episode AS T1 INNER JOIN Award AS T2 ON T1.episode_id = T2.episode_id WHERE T2.result = 'Winner' GROUP BY T1.episode_id ORDER BY COUNT(T2.award_id) DESC LIMIT 1
[ "What", "is", "the", "episode", "rating", "with", "the", "most", "award", "won", "?" ]
[ { "id": 0, "type": "column", "value": "episode_id" }, { "id": 6, "type": "column", "value": "award_id" }, { "id": 2, "type": "table", "value": "episode" }, { "id": 1, "type": "column", "value": "rating" }, { "id": 4, "type": "column", "value": "result" }, { "id": 5, "type": "value", "value": "Winner" }, { "id": 3, "type": "table", "value": "award" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "B-COLUMN", "B-COLUMN", "O", "O", "O", "O" ]
15,063
music_tracker
bird:train.json:2079
List the name of artists who have released albums and mixtape from 1980 to 1985 in "dance" genre.
SELECT COUNT(T1.artist) FROM torrents AS T1 INNER JOIN tags AS T2 ON T1.id = T2.id WHERE T2.tag = 'dance' AND T1.groupYear BETWEEN 1980 AND 1985 AND T1.releaseType LIKE 'album' OR T1.releaseType LIKE 'mixtape'
[ "List", "the", "name", "of", "artists", "who", "have", "released", "albums", "and", "mixtape", "from", "1980", "to", "1985", "in", "\"", "dance", "\"", "genre", "." ]
[ { "id": 4, "type": "column", "value": "releasetype" }, { "id": 8, "type": "column", "value": "groupyear" }, { "id": 0, "type": "table", "value": "torrents" }, { "id": 5, "type": "value", "value": "mixtape" }, { "id": 2, "type": "column", "value": "artist" }, { "id": 7, "type": "value", "value": "dance" }, { "id": 11, "type": "value", "value": "album" }, { "id": 1, "type": "table", "value": "tags" }, { "id": 9, "type": "value", "value": "1980" }, { "id": 10, "type": "value", "value": "1985" }, { "id": 6, "type": "column", "value": "tag" }, { "id": 3, "type": "column", "value": "id" } ]
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[ "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "O", "B-TABLE", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O" ]
10,647
books
bird:train.json:5930
List the title of the earliest published Japanese book.
SELECT T1.title FROM book AS T1 INNER JOIN book_language AS T2 ON T1.language_id = T2.language_id WHERE T2.language_name = 'Japanese' ORDER BY T1.publication_date ASC LIMIT 1
[ "List", "the", "title", "of", "the", "earliest", "published", "Japanese", "book", "." ]
[ { "id": 5, "type": "column", "value": "publication_date" }, { "id": 2, "type": "table", "value": "book_language" }, { "id": 3, "type": "column", "value": "language_name" }, { "id": 6, "type": "column", "value": "language_id" }, { "id": 4, "type": "value", "value": "Japanese" }, { "id": 0, "type": "column", "value": "title" }, { "id": 1, "type": "table", "value": "book" } ]
[ { "entity_id": 0, "token_idxs": [ 2 ] }, { "entity_id": 1, "token_idxs": [ 8 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 7 ] }, { "entity_id": 5, "token_idxs": [ 6 ] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "O", "O", "O", "B-COLUMN", "B-VALUE", "B-TABLE", "O" ]
14,327
store_1
spider:train_spider.json:601
List top 10 employee work longest in the company. List employee's first and last name.
SELECT first_name , last_name FROM employees ORDER BY hire_date ASC LIMIT 10;
[ "List", "top", "10", "employee", "work", "longest", "in", "the", "company", ".", "List", "employee", "'s", "first", "and", "last", "name", "." ]
[ { "id": 1, "type": "column", "value": "first_name" }, { "id": 0, "type": "table", "value": "employees" }, { "id": 2, "type": "column", "value": "last_name" }, { "id": 3, "type": "column", "value": "hire_date" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 7 ] }, { "entity_id": 2, "token_idxs": [ 13 ] }, { "entity_id": 3, "token_idxs": [ 2 ] }, { "entity_id": 4, "token_idxs": [ 12 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "O" ]
14,097
bike_share_1
bird:train.json:9074
List out all end stations for a bicycle that were making a trip starting from 2nd at South Park station? Only retain the unique value.
SELECT DISTINCT end_station_name FROM trip WHERE start_station_name = '2nd at South Park'
[ "List", "out", "all", "end", "stations", "for", "a", "bicycle", "that", "were", "making", "a", "trip", "starting", "from", "2nd", "at", "South", "Park", "station", "?", "Only", "retain", "the", "unique", "value", "." ]
[ { "id": 2, "type": "column", "value": "start_station_name" }, { "id": 3, "type": "value", "value": "2nd at South Park" }, { "id": 1, "type": "column", "value": "end_station_name" }, { "id": 0, "type": "table", "value": "trip" } ]
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[ "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "B-COLUMN", "O", "O", "O", "O", "O", "O", "O" ]
14,834
customers_card_transactions
spider:train_spider.json:730
What are the different card type codes?
SELECT DISTINCT card_type_code FROM Customers_Cards
[ "What", "are", "the", "different", "card", "type", "codes", "?" ]
[ { "id": 0, "type": "table", "value": "customers_cards" }, { "id": 1, "type": "column", "value": "card_type_code" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 4, 5, 6 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O" ]
12,634
university_basketball
spider:train_spider.json:987
What is the highest acc percent score in the competition?
SELECT acc_percent FROM basketball_match ORDER BY acc_percent DESC LIMIT 1
[ "What", "is", "the", "highest", "acc", "percent", "score", "in", "the", "competition", "?" ]
[ { "id": 0, "type": "table", "value": "basketball_match" }, { "id": 1, "type": "column", "value": "acc_percent" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O", "O" ]
9
shipping
bird:train.json:5667
What model year of truck delivered the ship ID 1233?
SELECT T1.model_year FROM truck AS T1 INNER JOIN shipment AS T2 ON T1.truck_id = T2.truck_id WHERE T2.ship_id = '1233'
[ "What", "model", "year", "of", "truck", "delivered", "the", "ship", "ID", "1233", "?" ]
[ { "id": 0, "type": "column", "value": "model_year" }, { "id": 2, "type": "table", "value": "shipment" }, { "id": 5, "type": "column", "value": "truck_id" }, { "id": 3, "type": "column", "value": "ship_id" }, { "id": 1, "type": "table", "value": "truck" }, { "id": 4, "type": "value", "value": "1233" } ]
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[ "O", "B-COLUMN", "I-COLUMN", "O", "B-TABLE", "O", "O", "B-COLUMN", "I-COLUMN", "B-VALUE", "O" ]
14,494
movie_3
bird:train.json:9131
Among the films that the customer RUTH MARTINEZ has rented, what is the title of the one with the highest replacement cost?
SELECT T4.title FROM customer AS T1 INNER JOIN rental AS T2 ON T1.customer_id = T2.customer_id INNER JOIN inventory AS T3 ON T2.inventory_id = T3.inventory_id INNER JOIN film AS T4 ON T3.film_id = T4.film_id WHERE T1.first_name = 'RUTH' AND T1.last_name = 'MARTINEZ' ORDER BY T4.replacement_cost DESC LIMIT 1
[ "Among", "the", "films", "that", "the", "customer", "RUTH", "MARTINEZ", "has", "rented", ",", "what", "is", "the", "title", "of", "the", "one", "with", "the", "highest", "replacement", "cost", "?" ]
[ { "id": 2, "type": "column", "value": "replacement_cost" }, { "id": 11, "type": "column", "value": "inventory_id" }, { "id": 12, "type": "column", "value": "customer_id" }, { "id": 5, "type": "column", "value": "first_name" }, { "id": 3, "type": "table", "value": "inventory" }, { "id": 7, "type": "column", "value": "last_name" }, { "id": 8, "type": "value", "value": "MARTINEZ" }, { "id": 9, "type": "table", "value": "customer" }, { "id": 4, "type": "column", "value": "film_id" }, { "id": 10, "type": "table", "value": "rental" }, { "id": 0, "type": "column", "value": "title" }, { "id": 1, "type": "table", "value": "film" }, { "id": 6, "type": "value", "value": "RUTH" } ]
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[ "O", "O", "B-TABLE", "O", "O", "B-TABLE", "B-VALUE", "B-VALUE", "O", "B-TABLE", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O" ]
128
college_2
spider:train_spider.json:1419
What are the ids of courses without prerequisites?
SELECT course_id FROM course EXCEPT SELECT course_id FROM prereq
[ "What", "are", "the", "ids", "of", "courses", "without", "prerequisites", "?" ]
[ { "id": 2, "type": "column", "value": "course_id" }, { "id": 0, "type": "table", "value": "course" }, { "id": 1, "type": "table", "value": "prereq" } ]
[ { "entity_id": 0, "token_idxs": [ 5 ] }, { "entity_id": 1, "token_idxs": [ 7 ] }, { "entity_id": 2, "token_idxs": [ 3 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "B-TABLE", "O" ]
10,844
address
bird:train.json:5231
Calculate the percentage of congress representatives from the Democrat party. Among them, how many postal points are in the Hawaii state?
SELECT CAST(SUM(CASE WHEN T1.party = 'Democrat' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*), SUM(CASE WHEN T1.state = 'Hawaii' THEN 1 ELSE 0 END) FROM congress AS T1 INNER JOIN zip_congress AS T2 ON T1.cognress_rep_id = T2.district
[ "Calculate", "the", "percentage", "of", "congress", "representatives", "from", "the", "Democrat", "party", ".", "Among", "them", ",", "how", "many", "postal", "points", "are", "in", "the", "Hawaii", "state", "?" ]
[ { "id": 2, "type": "column", "value": "cognress_rep_id" }, { "id": 1, "type": "table", "value": "zip_congress" }, { "id": 0, "type": "table", "value": "congress" }, { "id": 3, "type": "column", "value": "district" }, { "id": 10, "type": "value", "value": "Democrat" }, { "id": 8, "type": "value", "value": "Hawaii" }, { "id": 7, "type": "column", "value": "state" }, { "id": 9, "type": "column", "value": "party" }, { "id": 4, "type": "value", "value": "100" }, { "id": 5, "type": "value", "value": "0" }, { "id": 6, "type": "value", "value": "1" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "B-COLUMN", "O", "O", "O", "O", "B-VALUE", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O" ]
3,164
bakery_1
bird:test.json:1570
Give the ids of cookes that are cheaper than any croissant.
SELECT id FROM goods WHERE food = "Cookie" AND price < (SELECT min(price) FROM goods WHERE food = 'Croissant')
[ "Give", "the", "ids", "of", "cookes", "that", "are", "cheaper", "than", "any", "croissant", "." ]
[ { "id": 5, "type": "value", "value": "Croissant" }, { "id": 3, "type": "column", "value": "Cookie" }, { "id": 0, "type": "table", "value": "goods" }, { "id": 4, "type": "column", "value": "price" }, { "id": 2, "type": "column", "value": "food" }, { "id": 1, "type": "column", "value": "id" } ]
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[ "O", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O", "O", "O", "O", "B-VALUE", "O" ]
11,169
loan_1
spider:train_spider.json:3009
What is the total number of customers across banks?
SELECT sum(no_of_customers) FROM bank
[ "What", "is", "the", "total", "number", "of", "customers", "across", "banks", "?" ]
[ { "id": 1, "type": "column", "value": "no_of_customers" }, { "id": 0, "type": "table", "value": "bank" } ]
[ { "entity_id": 0, "token_idxs": [ 3, 6 ] }, { "entity_id": 1, "token_idxs": [ 9 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-TABLE", "O", "O", "B-TABLE", "O", "O", "B-COLUMN" ]
2,426
music_tracker
bird:train.json:2055
Please list the releases that have been downloaded for more than 20000 times.
SELECT groupName FROM torrents WHERE totalSnatched > 20000
[ "Please", "list", "the", "releases", "that", "have", "been", "downloaded", "for", "more", "than", "20000", "times", "." ]
[ { "id": 2, "type": "column", "value": "totalsnatched" }, { "id": 1, "type": "column", "value": "groupname" }, { "id": 0, "type": "table", "value": "torrents" }, { "id": 3, "type": "value", "value": "20000" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-VALUE", "O", "O" ]
7,953
human_resources
bird:train.json:8961
If Jose Rodriguez tried his best, how many percentage can his salary raise without changing his position?
SELECT 100 * (CAST(REPLACE(SUBSTR(T2.maxsalary, 4), ',', '') AS REAL) - CAST(REPLACE(SUBSTR(T1.salary, 4), ',', '') AS REAL)) / CAST(REPLACE(SUBSTR(T1.salary, 4), ',', '') AS REAL) AS per FROM employee AS T1 INNER JOIN position AS T2 ON T1.positionID = T2.positionID WHERE T1.firstname = 'Jose' AND T1.lastname = 'Rodriguez'
[ "If", "Jose", "Rodriguez", "tried", "his", "best", ",", "how", "many", "percentage", "can", "his", "salary", "raise", "without", "changing", "his", "position", "?" ]
[ { "id": 2, "type": "column", "value": "positionid" }, { "id": 3, "type": "column", "value": "firstname" }, { "id": 6, "type": "value", "value": "Rodriguez" }, { "id": 11, "type": "column", "value": "maxsalary" }, { "id": 0, "type": "table", "value": "employee" }, { "id": 1, "type": "table", "value": "position" }, { "id": 5, "type": "column", "value": "lastname" }, { "id": 9, "type": "column", "value": "salary" }, { "id": 4, "type": "value", "value": "Jose" }, { "id": 7, "type": "value", "value": "100" }, { "id": 8, "type": "value", "value": "," }, { "id": 10, "type": "value", "value": "4" } ]
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[ "O", "B-VALUE", "B-VALUE", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "B-TABLE", "O" ]
12,753
activity_1
spider:train_spider.json:6761
Show the ids for all the faculty members who have at least 2 students.
SELECT T1.FacID FROM Faculty AS T1 JOIN Student AS T2 ON T1.FacID = T2.advisor GROUP BY T1.FacID HAVING count(*) >= 2
[ "Show", "the", "ids", "for", "all", "the", "faculty", "members", "who", "have", "at", "least", "2", "students", "." ]
[ { "id": 1, "type": "table", "value": "faculty" }, { "id": 2, "type": "table", "value": "student" }, { "id": 4, "type": "column", "value": "advisor" }, { "id": 0, "type": "column", "value": "facid" }, { "id": 3, "type": "value", "value": "2" } ]
[ { "entity_id": 0, "token_idxs": [ 2 ] }, { "entity_id": 1, "token_idxs": [ 6 ] }, { "entity_id": 2, "token_idxs": [ 13 ] }, { "entity_id": 3, "token_idxs": [ 12 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "B-VALUE", "B-TABLE", "O" ]
11,612
works_cycles
bird:train.json:7279
How many products are out of stock?
SELECT COUNT(ProductID) FROM ProductVendor WHERE OnOrderQty = 0
[ "How", "many", "products", "are", "out", "of", "stock", "?" ]
[ { "id": 0, "type": "table", "value": "productvendor" }, { "id": 1, "type": "column", "value": "onorderqty" }, { "id": 3, "type": "column", "value": "productid" }, { "id": 2, "type": "value", "value": "0" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 2 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "O", "O", "O", "O", "O" ]
11,864
legislator
bird:train.json:4876
How many districts did John Conyers, Jr. serve in total?
SELECT COUNT(T3.district) FROM ( SELECT T2.district FROM current AS T1 INNER JOIN `current-terms` AS T2 ON T1.bioguide_id = T2.bioguide WHERE T1.official_full_name = 'John Conyers, Jr.' GROUP BY T2.district ) T3
[ "How", "many", "districts", "did", "John", "Conyers", ",", "Jr.", "serve", "in", "total", "?" ]
[ { "id": 3, "type": "column", "value": "official_full_name" }, { "id": 4, "type": "value", "value": "John Conyers, Jr." }, { "id": 2, "type": "table", "value": "current-terms" }, { "id": 5, "type": "column", "value": "bioguide_id" }, { "id": 0, "type": "column", "value": "district" }, { "id": 6, "type": "column", "value": "bioguide" }, { "id": 1, "type": "table", "value": "current" } ]
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[ "O", "O", "B-COLUMN", "O", "B-VALUE", "I-VALUE", "I-VALUE", "I-VALUE", "O", "O", "O", "O" ]
8,666
dorm_1
spider:train_spider.json:5681
How many diffrent dorm amenities are there?
SELECT count(*) FROM dorm_amenity
[ "How", "many", "diffrent", "dorm", "amenities", "are", "there", "?" ]
[ { "id": 0, "type": "table", "value": "dorm_amenity" } ]
[ { "entity_id": 0, "token_idxs": [ 3, 4 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-TABLE", "I-TABLE", "O", "O", "O" ]
7,373
bike_share_1
bird:train.json:9034
List the days in 2013 when rain and fog occurred together and find the id of bikes borrowed on these days.
SELECT T2.date, T1.bike_id FROM trip AS T1 INNER JOIN weather AS T2 ON T2.zip_code = T1.zip_code WHERE SUBSTR(CAST(T2.date AS TEXT), -4) = '2013' AND T2.events = 'Fog-Rain'
[ "List", "the", "days", "in", "2013", "when", "rain", "and", "fog", "occurred", "together", "and", "find", "the", "i", "d", "of", "bikes", "borrowed", "on", "these", "days", "." ]
[ { "id": 4, "type": "column", "value": "zip_code" }, { "id": 7, "type": "value", "value": "Fog-Rain" }, { "id": 1, "type": "column", "value": "bike_id" }, { "id": 3, "type": "table", "value": "weather" }, { "id": 6, "type": "column", "value": "events" }, { "id": 0, "type": "column", "value": "date" }, { "id": 2, "type": "table", "value": "trip" }, { "id": 5, "type": "value", "value": "2013" }, { "id": 8, "type": "value", "value": "-4" } ]
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[ "O", "O", "O", "O", "B-VALUE", "O", "O", "O", "B-VALUE", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "B-COLUMN", "O", "O", "O", "O", "O" ]
1,988
car_retails
bird:train.json:1583
How many customers who are in Norway and have credit line under 220000?
SELECT COUNT(creditLimit) FROM customers WHERE creditLimit < 220000 AND country = 'Norway'
[ "How", "many", "customers", "who", "are", "in", "Norway", "and", "have", "credit", "line", "under", "220000", "?" ]
[ { "id": 1, "type": "column", "value": "creditlimit" }, { "id": 0, "type": "table", "value": "customers" }, { "id": 3, "type": "column", "value": "country" }, { "id": 2, "type": "value", "value": "220000" }, { "id": 4, "type": "value", "value": "Norway" } ]
[ { "entity_id": 0, "token_idxs": [ 2 ] }, { "entity_id": 1, "token_idxs": [ 9 ] }, { "entity_id": 2, "token_idxs": [ 12 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 6 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-TABLE", "O", "O", "O", "B-VALUE", "O", "O", "B-COLUMN", "O", "O", "B-VALUE", "O" ]
4,499
retails
bird:train.json:6877
Please list the names of all the suppliers for parts under Brand#55.
SELECT T3.s_name FROM part AS T1 INNER JOIN partsupp AS T2 ON T1.p_partkey = T2.ps_partkey INNER JOIN supplier AS T3 ON T2.ps_suppkey = T3.s_suppkey WHERE T1.p_brand = 'Brand#55'
[ "Please", "list", "the", "names", "of", "all", "the", "suppliers", "for", "parts", "under", "Brand#55", "." ]
[ { "id": 6, "type": "column", "value": "ps_suppkey" }, { "id": 9, "type": "column", "value": "ps_partkey" }, { "id": 7, "type": "column", "value": "s_suppkey" }, { "id": 8, "type": "column", "value": "p_partkey" }, { "id": 1, "type": "table", "value": "supplier" }, { "id": 3, "type": "value", "value": "Brand#55" }, { "id": 5, "type": "table", "value": "partsupp" }, { "id": 2, "type": "column", "value": "p_brand" }, { "id": 0, "type": "column", "value": "s_name" }, { "id": 4, "type": "table", "value": "part" } ]
[ { "entity_id": 0, "token_idxs": [ 3 ] }, { "entity_id": 1, "token_idxs": [ 7 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 11 ] }, { "entity_id": 4, "token_idxs": [ 9 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "O", "O", "O", "B-TABLE", "O", "B-TABLE", "O", "B-VALUE", "O" ]
5,559
browser_web
spider:train_spider.json:1839
List the names of the browser that are compatible with both 'CACHEbox' and 'Fasterfox'.
SELECT T3.name FROM web_client_accelerator AS T1 JOIN accelerator_compatible_browser AS T2 ON T2.accelerator_id = T1.id JOIN browser AS T3 ON T2.browser_id = T3.id WHERE T1.name = 'CACHEbox' INTERSECT SELECT T3.name FROM web_client_accelerator AS T1 JOIN accelerator_compatible_browser AS T2 ON T2.accelerator_id = T1.id JOIN browser AS T3 ON T2.browser_id = T3.id WHERE T1.name = 'Fasterfox'
[ "List", "the", "names", "of", "the", "browser", "that", "are", "compatible", "with", "both", "'", "CACHEbox", "'", "and", "'", "Fasterfox", "'", "." ]
[ { "id": 5, "type": "table", "value": "accelerator_compatible_browser" }, { "id": 4, "type": "table", "value": "web_client_accelerator" }, { "id": 8, "type": "column", "value": "accelerator_id" }, { "id": 6, "type": "column", "value": "browser_id" }, { "id": 3, "type": "value", "value": "Fasterfox" }, { "id": 2, "type": "value", "value": "CACHEbox" }, { "id": 1, "type": "table", "value": "browser" }, { "id": 0, "type": "column", "value": "name" }, { "id": 7, "type": "column", "value": "id" } ]
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[ "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "B-TABLE", "O", "O", "O", "B-VALUE", "O", "O", "O", "B-VALUE", "O", "O" ]
14,101
olympics
bird:train.json:5078
Provide the age of the tallest competitor.
SELECT T2.age FROM person AS T1 INNER JOIN games_competitor AS T2 ON T1.id = T2.person_id ORDER BY T1.height DESC LIMIT 1
[ "Provide", "the", "age", "of", "the", "tallest", "competitor", "." ]
[ { "id": 2, "type": "table", "value": "games_competitor" }, { "id": 5, "type": "column", "value": "person_id" }, { "id": 1, "type": "table", "value": "person" }, { "id": 3, "type": "column", "value": "height" }, { "id": 0, "type": "column", "value": "age" }, { "id": 4, "type": "column", "value": "id" } ]
[ { "entity_id": 0, "token_idxs": [ 2 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 6 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "O", "O", "O", "B-TABLE", "O" ]
5,085
vehicle_rent
bird:test.json:434
Return the name of the discount that corresponds to the most rental history records.
SELECT T2.name FROM renting_history AS T1 JOIN discount AS T2 ON T1.discount_id = T2.id GROUP BY T2.id ORDER BY count(*) DESC LIMIT 1
[ "Return", "the", "name", "of", "the", "discount", "that", "corresponds", "to", "the", "most", "rental", "history", "records", "." ]
[ { "id": 2, "type": "table", "value": "renting_history" }, { "id": 4, "type": "column", "value": "discount_id" }, { "id": 3, "type": "table", "value": "discount" }, { "id": 1, "type": "column", "value": "name" }, { "id": 0, "type": "column", "value": "id" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [ 2 ] }, { "entity_id": 2, "token_idxs": [ 11, 12 ] }, { "entity_id": 3, "token_idxs": [ 5 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "B-TABLE", "I-TABLE", "O", "O" ]
7,668
allergy_1
spider:train_spider.json:530
How many male students (sex is 'M') are allergic to any type of food?
SELECT count(*) FROM Student WHERE sex = "M" AND StuID IN (SELECT StuID FROM Has_allergy AS T1 JOIN Allergy_Type AS T2 ON T1.Allergy = T2.Allergy WHERE T2.allergytype = "food")
[ "How", "many", "male", "students", "(", "sex", "is", "'", "M", "'", ")", "are", "allergic", "to", "any", "type", "of", "food", "?" ]
[ { "id": 5, "type": "table", "value": "allergy_type" }, { "id": 4, "type": "table", "value": "has_allergy" }, { "id": 6, "type": "column", "value": "allergytype" }, { "id": 0, "type": "table", "value": "student" }, { "id": 8, "type": "column", "value": "allergy" }, { "id": 3, "type": "column", "value": "stuid" }, { "id": 7, "type": "column", "value": "food" }, { "id": 1, "type": "column", "value": "sex" }, { "id": 2, "type": "column", "value": "M" } ]
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[ "O", "O", "O", "B-TABLE", "O", "B-COLUMN", "O", "O", "B-COLUMN", "O", "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "O", "B-COLUMN", "O" ]
15,943
retails
bird:train.json:6776
What is the average discount for the parts made by Manufacturer#5?
SELECT AVG(T3.l_discount) FROM part AS T1 INNER JOIN partsupp AS T2 ON T1.p_partkey = T2.ps_partkey INNER JOIN lineitem AS T3 ON T2.ps_suppkey = T3.l_suppkey WHERE T1.p_mfgr = 'Manufacturer#5'
[ "What", "is", "the", "average", "discount", "for", "the", "parts", "made", "by", "Manufacturer#5", "?" ]
[ { "id": 2, "type": "value", "value": "Manufacturer#5" }, { "id": 3, "type": "column", "value": "l_discount" }, { "id": 6, "type": "column", "value": "ps_suppkey" }, { "id": 9, "type": "column", "value": "ps_partkey" }, { "id": 7, "type": "column", "value": "l_suppkey" }, { "id": 8, "type": "column", "value": "p_partkey" }, { "id": 0, "type": "table", "value": "lineitem" }, { "id": 5, "type": "table", "value": "partsupp" }, { "id": 1, "type": "column", "value": "p_mfgr" }, { "id": 4, "type": "table", "value": "part" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "B-VALUE", "O" ]
1,612
chicago_crime
bird:train.json:8694
List all the crimes of the narcotic type that exist.
SELECT secondary_description FROM IUCR WHERE primary_description = 'NARCOTICS' GROUP BY secondary_description
[ "List", "all", "the", "crimes", "of", "the", "narcotic", "type", "that", "exist", "." ]
[ { "id": 1, "type": "column", "value": "secondary_description" }, { "id": 2, "type": "column", "value": "primary_description" }, { "id": 3, "type": "value", "value": "NARCOTICS" }, { "id": 0, "type": "table", "value": "iucr" } ]
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[ "O", "O", "O", "O", "O", "O", "B-VALUE", "O", "O", "O", "O" ]
12,590
baseball_1
spider:train_spider.json:3676
Find the number of tied games (the value of "ties" is '1') in 1885 postseason.
SELECT count(*) FROM postseason WHERE YEAR = 1885 AND ties = 1;
[ "Find", "the", "number", "of", "tied", "games", "(", "the", "value", "of", "\"", "ties", "\"", "is", "'", "1", "'", ")", "in", "1885", "postseason", "." ]
[ { "id": 0, "type": "table", "value": "postseason" }, { "id": 1, "type": "column", "value": "year" }, { "id": 2, "type": "value", "value": "1885" }, { "id": 3, "type": "column", "value": "ties" }, { "id": 4, "type": "value", "value": "1" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "B-VALUE", "O", "O", "O", "B-VALUE", "B-TABLE", "O" ]
7,633
works_cycles
bird:train.json:7111
How many vendors are having their products ordered with an average delivery time of 25 days?
SELECT COUNT(DISTINCT BusinessEntityID) FROM ProductVendor WHERE AverageLeadTime = 25
[ "How", "many", "vendors", "are", "having", "their", "products", "ordered", "with", "an", "average", "delivery", "time", "of", "25", "days", "?" ]
[ { "id": 3, "type": "column", "value": "businessentityid" }, { "id": 1, "type": "column", "value": "averageleadtime" }, { "id": 0, "type": "table", "value": "productvendor" }, { "id": 2, "type": "value", "value": "25" } ]
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[ "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "B-COLUMN", "O", "O", "O", "B-VALUE", "O", "O" ]
96
world_development_indicators
bird:train.json:2216
What are the full names of the countries in South Asia that belongs to the low income group?
SELECT LongName FROM Country WHERE IncomeGroup = 'Low income' AND Region = 'South Asia'
[ "What", "are", "the", "full", "names", "of", "the", "countries", "in", "South", "Asia", "that", "belongs", "to", "the", "low", "income", "group", "?" ]
[ { "id": 2, "type": "column", "value": "incomegroup" }, { "id": 3, "type": "value", "value": "Low income" }, { "id": 5, "type": "value", "value": "South Asia" }, { "id": 1, "type": "column", "value": "longname" }, { "id": 0, "type": "table", "value": "country" }, { "id": 4, "type": "column", "value": "region" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "B-TABLE", "O", "B-VALUE", "I-VALUE", "O", "O", "O", "O", "B-VALUE", "I-VALUE", "B-COLUMN", "O" ]
475
works_cycles
bird:train.json:7181
What is the stocked quantity of products manufactured from location ID 40?
SELECT COUNT(*) FROM WorkOrderRouting AS T1 INNER JOIN BillOfMaterials AS T2 ON T1.LocationID = T2.ProductAssemblyID INNER JOIN WorkOrder AS T3 ON T3.WorkOrderID = T1.WorkOrderID WHERE T1.LocationID = 40
[ "What", "is", "the", "stocked", "quantity", "of", "products", "manufactured", "from", "location", "ID", "40", "?" ]
[ { "id": 6, "type": "column", "value": "productassemblyid" }, { "id": 3, "type": "table", "value": "workorderrouting" }, { "id": 4, "type": "table", "value": "billofmaterials" }, { "id": 5, "type": "column", "value": "workorderid" }, { "id": 1, "type": "column", "value": "locationid" }, { "id": 0, "type": "table", "value": "workorder" }, { "id": 2, "type": "value", "value": "40" } ]
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[ "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "I-COLUMN", "B-VALUE", "O" ]
15,327
institution_sports
bird:test.json:1646
What are the names of institutions, ordered by the years in which they were founded?
SELECT Name FROM institution ORDER BY Founded ASC
[ "What", "are", "the", "names", "of", "institutions", ",", "ordered", "by", "the", "years", "in", "which", "they", "were", "founded", "?" ]
[ { "id": 0, "type": "table", "value": "institution" }, { "id": 2, "type": "column", "value": "founded" }, { "id": 1, "type": "column", "value": "name" } ]
[ { "entity_id": 0, "token_idxs": [ 5 ] }, { "entity_id": 1, "token_idxs": [ 3 ] }, { "entity_id": 2, "token_idxs": [ 15 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "O" ]
14,746
works_cycles
bird:train.json:7314
List all staff in the Shipping and Receiving department who are hired in 2009.
SELECT T1.FirstName, T1.LastName FROM Person AS T1 INNER JOIN Employee AS T2 ON T1.BusinessEntityID = T2.BusinessEntityID INNER JOIN EmployeeDepartmentHistory AS T3 ON T2.BusinessEntityID = T3.BusinessEntityID INNER JOIN Department AS T4 ON T3.DepartmentID = T4.DepartmentID WHERE STRFTIME('%Y', T2.HireDate) = '2009' AND T4.Name = 'Shipping and Receiving'
[ "List", "all", "staff", "in", "the", "Shipping", "and", "Receiving", "department", "who", "are", "hired", "in", "2009", "." ]
[ { "id": 3, "type": "table", "value": "employeedepartmenthistory" }, { "id": 7, "type": "value", "value": "Shipping and Receiving" }, { "id": 10, "type": "column", "value": "businessentityid" }, { "id": 4, "type": "column", "value": "departmentid" }, { "id": 2, "type": "table", "value": "department" }, { "id": 0, "type": "column", "value": "firstname" }, { "id": 1, "type": "column", "value": "lastname" }, { "id": 9, "type": "table", "value": "employee" }, { "id": 12, "type": "column", "value": "hiredate" }, { "id": 8, "type": "table", "value": "person" }, { "id": 5, "type": "value", "value": "2009" }, { "id": 6, "type": "column", "value": "name" }, { "id": 11, "type": "value", "value": "%Y" } ]
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[ "O", "O", "O", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "B-TABLE", "O", "O", "B-COLUMN", "O", "B-VALUE", "O" ]
7,886
flight_company
spider:train_spider.json:6374
What are the ids and names of the companies that operated more than one flight?
SELECT T1.id , T1.name FROM operate_company AS T1 JOIN flight AS t2 ON T1.id = T2.company_id GROUP BY T1.id HAVING count(*) > 1
[ "What", "are", "the", "ids", "and", "names", "of", "the", "companies", "that", "operated", "more", "than", "one", "flight", "?" ]
[ { "id": 2, "type": "table", "value": "operate_company" }, { "id": 5, "type": "column", "value": "company_id" }, { "id": 3, "type": "table", "value": "flight" }, { "id": 1, "type": "column", "value": "name" }, { "id": 0, "type": "column", "value": "id" }, { "id": 4, "type": "value", "value": "1" } ]
[ { "entity_id": 0, "token_idxs": [ 3 ] }, { "entity_id": 1, "token_idxs": [ 5 ] }, { "entity_id": 2, "token_idxs": [ 10 ] }, { "entity_id": 3, "token_idxs": [ 14 ] }, { "entity_id": 4, "token_idxs": [ 13 ] }, { "entity_id": 5, "token_idxs": [ 8 ] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "O", "B-VALUE", "B-TABLE", "O" ]
10,770
talkingdata
bird:train.json:1243
Which brand is most common among people in their twenties?
SELECT T.phone_brand FROM ( SELECT T2.phone_brand, COUNT(T2.phone_brand) AS num FROM gender_age AS T1 INNER JOIN phone_brand_device_model2 AS T2 ON T1.device_id = T2.device_id WHERE T1.age BETWEEN 20 AND 30 GROUP BY T2.phone_brand ) AS T ORDER BY T.num DESC LIMIT 1
[ "Which", "brand", "is", "most", "common", "among", "people", "in", "their", "twenties", "?" ]
[ { "id": 3, "type": "table", "value": "phone_brand_device_model2" }, { "id": 0, "type": "column", "value": "phone_brand" }, { "id": 2, "type": "table", "value": "gender_age" }, { "id": 7, "type": "column", "value": "device_id" }, { "id": 1, "type": "column", "value": "num" }, { "id": 4, "type": "column", "value": "age" }, { "id": 5, "type": "value", "value": "20" }, { "id": 6, "type": "value", "value": "30" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "O", "O", "B-COLUMN", "O", "O", "O" ]
2,724
thrombosis_prediction
bird:dev.json:1243
For all patients who are older than 55 years old, what is the percentage of female who has abnormal prothrombin time (PT)?
SELECT CAST(SUM(CASE WHEN T2.PT >= 14 AND T1.SEX = 'F' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*) FROM Patient AS T1 INNER JOIN Laboratory AS T2 ON T1.ID = T2.ID WHERE STRFTIME('%Y', CURRENT_TIMESTAMP) - STRFTIME('%Y', T1.Birthday) > 55
[ "For", "all", "patients", "who", "are", "older", "than", "55", "years", "old", ",", "what", "is", "the", "percentage", "of", "female", "who", "has", "abnormal", "prothrombin", "time", "(", "PT", ")", "?" ]
[ { "id": 1, "type": "table", "value": "laboratory" }, { "id": 6, "type": "column", "value": "birthday" }, { "id": 0, "type": "table", "value": "patient" }, { "id": 4, "type": "value", "value": "100" }, { "id": 11, "type": "column", "value": "sex" }, { "id": 2, "type": "value", "value": "55" }, { "id": 3, "type": "column", "value": "id" }, { "id": 5, "type": "value", "value": "%Y" }, { "id": 9, "type": "column", "value": "pt" }, { "id": 10, "type": "value", "value": "14" }, { "id": 7, "type": "value", "value": "0" }, { "id": 8, "type": "value", "value": "1" }, { "id": 12, "type": "value", "value": "F" } ]
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[ "O", "O", "B-TABLE", "O", "O", "O", "O", "B-VALUE", "B-VALUE", "O", "O", "O", "O", "O", "O", "O", "B-VALUE", "O", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "I-COLUMN", "I-COLUMN", "O" ]
11,597
chinook_1
spider:train_spider.json:853
How many distinct cities does the employees live in?
SELECT COUNT(DISTINCT city) FROM EMPLOYEE
[ "How", "many", "distinct", "cities", "does", "the", "employees", "live", "in", "?" ]
[ { "id": 0, "type": "table", "value": "employee" }, { "id": 1, "type": "column", "value": "city" } ]
[ { "entity_id": 0, "token_idxs": [ 6 ] }, { "entity_id": 1, "token_idxs": [ 3 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "O" ]
4,279
movie_3
bird:train.json:9214
Mention the language of Untouchables Sunrise film and calculate its rental cost per day.
SELECT T2.name, T1.replacement_cost / T1.rental_duration AS cost FROM film AS T1 INNER JOIN language AS T2 ON T1.language_id = T2.language_id WHERE T1.title = 'UNTOUCHABLES SUNRISE'
[ "Mention", "the", "language", "of", "Untouchables", "Sunrise", "film", "and", "calculate", "its", "rental", "cost", "per", "day", "." ]
[ { "id": 4, "type": "value", "value": "UNTOUCHABLES SUNRISE" }, { "id": 5, "type": "column", "value": "replacement_cost" }, { "id": 6, "type": "column", "value": "rental_duration" }, { "id": 7, "type": "column", "value": "language_id" }, { "id": 2, "type": "table", "value": "language" }, { "id": 3, "type": "column", "value": "title" }, { "id": 0, "type": "column", "value": "name" }, { "id": 1, "type": "table", "value": "film" } ]
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[ "O", "O", "B-TABLE", "O", "B-VALUE", "I-VALUE", "B-TABLE", "O", "O", "O", "B-COLUMN", "B-COLUMN", "O", "O", "O" ]
12,257
donor
bird:train.json:3262
Which resource type is commonly bought by the Los Angeles Unified School District?
SELECT T1.project_resource_type FROM resources AS T1 INNER JOIN projects AS T2 ON T1.projectid = T2.projectid WHERE T2.school_district = 'Los Angeles Unif Sch Dist' GROUP BY T2.school_district ORDER BY COUNT(T1.project_resource_type) DESC LIMIT 1
[ "Which", "resource", "type", "is", "commonly", "bought", "by", "the", "Los", "Angeles", "Unified", "School", "District", "?" ]
[ { "id": 4, "type": "value", "value": "Los Angeles Unif Sch Dist" }, { "id": 1, "type": "column", "value": "project_resource_type" }, { "id": 0, "type": "column", "value": "school_district" }, { "id": 2, "type": "table", "value": "resources" }, { "id": 5, "type": "column", "value": "projectid" }, { "id": 3, "type": "table", "value": "projects" } ]
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[ "O", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "B-COLUMN", "I-COLUMN", "O" ]
5,763
bakery_1
bird:test.json:1490
What is the id and flavor of the cheapest cookie?
SELECT id , flavor FROM goods WHERE food = "Cookie" ORDER BY price LIMIT 1
[ "What", "is", "the", "i", "d", "and", "flavor", "of", "the", "cheapest", "cookie", "?" ]
[ { "id": 2, "type": "column", "value": "flavor" }, { "id": 4, "type": "column", "value": "Cookie" }, { "id": 0, "type": "table", "value": "goods" }, { "id": 5, "type": "column", "value": "price" }, { "id": 3, "type": "column", "value": "food" }, { "id": 1, "type": "column", "value": "id" } ]
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[ "O", "B-TABLE", "O", "B-COLUMN", "I-COLUMN", "O", "B-COLUMN", "O", "O", "O", "B-COLUMN", "O" ]
1,343
thrombosis_prediction
bird:dev.json:1283
For the patients with the normal glutamic pylvic transaminase level, how many of them are male?
SELECT COUNT(T1.ID) FROM Patient AS T1 INNER JOIN Laboratory AS T2 ON T1.ID = T2.ID WHERE T2.GOT < 60 AND T1.SEX = 'M'
[ "For", "the", "patients", "with", "the", "normal", "glutamic", "pylvic", "transaminase", "level", ",", "how", "many", "of", "them", "are", "male", "?" ]
[ { "id": 1, "type": "table", "value": "laboratory" }, { "id": 0, "type": "table", "value": "patient" }, { "id": 3, "type": "column", "value": "got" }, { "id": 5, "type": "column", "value": "sex" }, { "id": 2, "type": "column", "value": "id" }, { "id": 4, "type": "value", "value": "60" }, { "id": 6, "type": "value", "value": "M" } ]
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[ "O", "O", "O", "B-TABLE", "I-TABLE", "I-TABLE", "I-TABLE", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "O", "O" ]
10,868
college_1
spider:train_spider.json:3284
What are the course codes for every class that the student with the last name Smithson took?
SELECT T1.crs_code FROM CLASS AS T1 JOIN enroll AS T2 ON T1.class_code = T2.class_code JOIN student AS T3 ON T3.stu_num = T2.stu_num WHERE T3.stu_lname = 'Smithson'
[ "What", "are", "the", "course", "codes", "for", "every", "class", "that", "the", "student", "with", "the", "last", "name", "Smithson", "took", "?" ]
[ { "id": 7, "type": "column", "value": "class_code" }, { "id": 2, "type": "column", "value": "stu_lname" }, { "id": 0, "type": "column", "value": "crs_code" }, { "id": 3, "type": "value", "value": "Smithson" }, { "id": 1, "type": "table", "value": "student" }, { "id": 6, "type": "column", "value": "stu_num" }, { "id": 5, "type": "table", "value": "enroll" }, { "id": 4, "type": "table", "value": "class" } ]
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[ "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-TABLE", "O", "O", "B-TABLE", "O", "O", "B-COLUMN", "I-COLUMN", "B-VALUE", "O", "O" ]
10,975
driving_school
spider:train_spider.json:6667
What is the date of birth of every customer whose status code is 'Good Customer'?
SELECT date_of_birth FROM Customers WHERE customer_status_code = 'Good Customer'
[ "What", "is", "the", "date", "of", "birth", "of", "every", "customer", "whose", "status", "code", "is", "'", "Good", "Customer", "'", "?" ]
[ { "id": 2, "type": "column", "value": "customer_status_code" }, { "id": 1, "type": "column", "value": "date_of_birth" }, { "id": 3, "type": "value", "value": "Good Customer" }, { "id": 0, "type": "table", "value": "customers" } ]
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[ "O", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "O", "B-COLUMN", "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-VALUE", "B-TABLE", "O", "O" ]
9,224
airline
bird:train.json:5839
Give the number of planes that took off from Los Angeles International airport on 2018/8/27.
SELECT SUM(CASE WHEN T2.FL_DATE = '2018/8/27' THEN 1 ELSE 0 END) AS count FROM Airports AS T1 INNER JOIN Airlines AS T2 ON T1.Code = T2.ORIGIN WHERE T1.Description = 'Los Angeles, CA: Los Angeles International'
[ "Give", "the", "number", "of", "planes", "that", "took", "off", "from", "Los", "Angeles", "International", "airport", "on", "2018/8/27", "." ]
[ { "id": 3, "type": "value", "value": "Los Angeles, CA: Los Angeles International" }, { "id": 2, "type": "column", "value": "description" }, { "id": 9, "type": "value", "value": "2018/8/27" }, { "id": 0, "type": "table", "value": "airports" }, { "id": 1, "type": "table", "value": "airlines" }, { "id": 8, "type": "column", "value": "fl_date" }, { "id": 5, "type": "column", "value": "origin" }, { "id": 4, "type": "column", "value": "code" }, { "id": 6, "type": "value", "value": "0" }, { "id": 7, "type": "value", "value": "1" } ]
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[ "O", "O", "B-TABLE", "O", "O", "O", "B-COLUMN", "B-VALUE", "O", "B-VALUE", "O", "O", "O", "O", "O", "O" ]
11,145
apartment_rentals
spider:train_spider.json:1249
How many bookings does each booking status have? List the booking status code and the number of corresponding bookings.
SELECT booking_status_code , COUNT(*) FROM Apartment_Bookings GROUP BY booking_status_code
[ "How", "many", "bookings", "does", "each", "booking", "status", "have", "?", "List", "the", "booking", "status", "code", "and", "the", "number", "of", "corresponding", "bookings", "." ]
[ { "id": 1, "type": "column", "value": "booking_status_code" }, { "id": 0, "type": "table", "value": "apartment_bookings" } ]
[ { "entity_id": 0, "token_idxs": [ 4 ] }, { "entity_id": 1, "token_idxs": [ 9 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [ 7 ] }, { "entity_id": 7, "token_idxs": [ 17, 18, 19, 20 ] }, { "entity_id": 8, "token_idxs": [ 16 ] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O" ]
14,385
college_1
spider:train_spider.json:3325
What is the last name of the student who got a grade A in the class with code 10018.
SELECT T1.stu_lname FROM student AS T1 JOIN enroll AS T2 ON T1.stu_num = T2.stu_num WHERE T2.enroll_grade = 'A' AND T2.class_code = 10018
[ "What", "is", "the", "last", "name", "of", "the", "student", "who", "got", "a", "grade", "A", "in", "the", "class", "with", "code", "10018", "." ]
[ { "id": 4, "type": "column", "value": "enroll_grade" }, { "id": 6, "type": "column", "value": "class_code" }, { "id": 0, "type": "column", "value": "stu_lname" }, { "id": 1, "type": "table", "value": "student" }, { "id": 3, "type": "column", "value": "stu_num" }, { "id": 2, "type": "table", "value": "enroll" }, { "id": 7, "type": "value", "value": "10018" }, { "id": 5, "type": "value", "value": "A" } ]
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[ "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "B-COLUMN", "B-VALUE", "O", "O", "B-COLUMN", "O", "B-COLUMN", "B-VALUE", "O" ]
9,932
public_review_platform
bird:train.json:3775
What percentage more for the "Women's Clothing" Yelp businesses to "Men's Clothing"?
SELECT CAST(SUM(CASE WHEN T2.category_name LIKE 'Women''s Clothing' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(T1.business_id) - CAST(SUM(CASE WHEN T2.category_name LIKE 'Men''s Clothing' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(T1.business_id) AS "more percentage" FROM Business_Categories AS T1 INNER JOIN Categories AS T2 ON T1.category_id = T2.category_id
[ "What", "percentage", "more", "for", "the", "\"", "Women", "'s", "Clothing", "\"", "Yelp", "businesses", "to", "\"", "Men", "'s", "Clothing", "\"", "?" ]
[ { "id": 0, "type": "table", "value": "business_categories" }, { "id": 8, "type": "value", "value": "Women's Clothing" }, { "id": 9, "type": "value", "value": "Men's Clothing" }, { "id": 7, "type": "column", "value": "category_name" }, { "id": 2, "type": "column", "value": "category_id" }, { "id": 4, "type": "column", "value": "business_id" }, { "id": 1, "type": "table", "value": "categories" }, { "id": 3, "type": "value", "value": "100" }, { "id": 5, "type": "value", "value": "0" }, { "id": 6, "type": "value", "value": "1" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 11 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [ 6, 7, 8 ] }, { "entity_id": 9, "token_idxs": [ 14, 15, 16 ] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "O", "O", "B-COLUMN", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "O", "O" ]
6,888
student_loan
bird:train.json:4480
How many students enlisted in the navy?
SELECT COUNT(name) FROM enlist WHERE organ = 'navy'
[ "How", "many", "students", "enlisted", "in", "the", "navy", "?" ]
[ { "id": 0, "type": "table", "value": "enlist" }, { "id": 1, "type": "column", "value": "organ" }, { "id": 2, "type": "value", "value": "navy" }, { "id": 3, "type": "column", "value": "name" } ]
[ { "entity_id": 0, "token_idxs": [ 3 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 6 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-TABLE", "O", "O", "B-VALUE", "O" ]
14,809
sales
bird:train.json:5429
Give the product's name brought by Aaron Alexander.
SELECT DISTINCT T1.Name FROM Products AS T1 INNER JOIN Sales AS T2 ON T1.ProductID = T2.ProductID INNER JOIN Customers AS T3 ON T2.CustomerID = T3.CustomerID WHERE T3.FirstName = 'Aaron' AND T3.LastName = 'Alexander'
[ "Give", "the", "product", "'s", "name", "brought", "by", "Aaron", "Alexander", "." ]
[ { "id": 4, "type": "column", "value": "customerid" }, { "id": 1, "type": "table", "value": "customers" }, { "id": 5, "type": "column", "value": "firstname" }, { "id": 8, "type": "value", "value": "Alexander" }, { "id": 9, "type": "column", "value": "productid" }, { "id": 2, "type": "table", "value": "products" }, { "id": 7, "type": "column", "value": "lastname" }, { "id": 3, "type": "table", "value": "sales" }, { "id": 6, "type": "value", "value": "Aaron" }, { "id": 0, "type": "column", "value": "name" } ]
[ { "entity_id": 0, "token_idxs": [ 4 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 2 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [ 7 ] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [ 8 ] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-TABLE", "O", "B-COLUMN", "O", "O", "B-VALUE", "B-VALUE", "O" ]
12,205
college_2
spider:train_spider.json:1418
Find the id of the courses that do not have any prerequisite?
SELECT course_id FROM course EXCEPT SELECT course_id FROM prereq
[ "Find", "the", "i", "d", "of", "the", "courses", "that", "do", "not", "have", "any", "prerequisite", "?" ]
[ { "id": 2, "type": "column", "value": "course_id" }, { "id": 0, "type": "table", "value": "course" }, { "id": 1, "type": "table", "value": "prereq" } ]
[ { "entity_id": 0, "token_idxs": [ 6 ] }, { "entity_id": 1, "token_idxs": [ 12 ] }, { "entity_id": 2, "token_idxs": [ 2, 3 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "B-TABLE", "O" ]
15,167
customers_and_orders
bird:test.json:287
What is the product type with least number of products?
SELECT product_type_code FROM Products GROUP BY product_type_code ORDER BY count(*) ASC LIMIT 1
[ "What", "is", "the", "product", "type", "with", "least", "number", "of", "products", "?" ]
[ { "id": 1, "type": "column", "value": "product_type_code" }, { "id": 0, "type": "table", "value": "products" } ]
[ { "entity_id": 0, "token_idxs": [ 9 ] }, { "entity_id": 1, "token_idxs": [ 3, 4 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O", "B-TABLE", "O" ]
3,872
address_1
bird:test.json:798
Give the state that has the most students.
SELECT T1.state FROM City AS T1 JOIN Student AS T2 ON T1.city_code = T2.city_code GROUP BY T1.state ORDER BY count(*) DESC LIMIT 1
[ "Give", "the", "state", "that", "has", "the", "most", "students", "." ]
[ { "id": 3, "type": "column", "value": "city_code" }, { "id": 2, "type": "table", "value": "student" }, { "id": 0, "type": "column", "value": "state" }, { "id": 1, "type": "table", "value": "city" } ]
[ { "entity_id": 0, "token_idxs": [ 2 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 7 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "O", "O", "O", "O", "B-TABLE", "O" ]
7,564
world_development_indicators
bird:train.json:2165
Which high income group countries are from Asia?
SELECT CountryCode, Region FROM Country WHERE (IncomeGroup = 'High income: OECD' OR IncomeGroup = 'High income: nonOECD') AND Region LIKE '%Asia%'
[ "Which", "high", "income", "group", "countries", "are", "from", "Asia", "?" ]
[ { "id": 6, "type": "value", "value": "High income: nonOECD" }, { "id": 5, "type": "value", "value": "High income: OECD" }, { "id": 1, "type": "column", "value": "countrycode" }, { "id": 4, "type": "column", "value": "incomegroup" }, { "id": 0, "type": "table", "value": "country" }, { "id": 2, "type": "column", "value": "region" }, { "id": 3, "type": "value", "value": "%Asia%" } ]
[ { "entity_id": 0, "token_idxs": [ 4 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [ 7 ] }, { "entity_id": 4, "token_idxs": [ 2, 3 ] }, { "entity_id": 5, "token_idxs": [ 1 ] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "B-VALUE", "B-COLUMN", "I-COLUMN", "B-TABLE", "O", "O", "B-VALUE", "O" ]
5,663
formula_1
spider:train_spider.json:2224
For each race name, What is the maximum fastest lap speed for races after 2004 ordered by year?
SELECT max(T2.fastestlapspeed) , T1.name , T1.year FROM races AS T1 JOIN results AS T2 ON T1.raceid = T2.raceid WHERE T1.year > 2014 GROUP BY T1.name ORDER BY T1.year
[ "For", "each", "race", "name", ",", "What", "is", "the", "maximum", "fastest", "lap", "speed", "for", "races", "after", "2004", "ordered", "by", "year", "?" ]
[ { "id": 5, "type": "column", "value": "fastestlapspeed" }, { "id": 3, "type": "table", "value": "results" }, { "id": 6, "type": "column", "value": "raceid" }, { "id": 2, "type": "table", "value": "races" }, { "id": 0, "type": "column", "value": "name" }, { "id": 1, "type": "column", "value": "year" }, { "id": 4, "type": "value", "value": "2014" } ]
[ { "entity_id": 0, "token_idxs": [ 3 ] }, { "entity_id": 1, "token_idxs": [ 18 ] }, { "entity_id": 2, "token_idxs": [ 13 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 15 ] }, { "entity_id": 5, "token_idxs": [ 9, 10, 11 ] }, { "entity_id": 6, "token_idxs": [ 2 ] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "B-COLUMN", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "B-TABLE", "O", "B-VALUE", "O", "O", "B-COLUMN", "O" ]
14,117
medicine_enzyme_interaction
spider:train_spider.json:954
What is the id and name of the enzyme that can interact with the most medicines as an activator?
SELECT T1.id , T1.name FROM enzyme AS T1 JOIN medicine_enzyme_interaction AS T2 ON T1.id = T2.enzyme_id WHERE T2.interaction_type = 'activitor' GROUP BY T1.id ORDER BY count(*) DESC LIMIT 1
[ "What", "is", "the", "i", "d", "and", "name", "of", "the", "enzyme", "that", "can", "interact", "with", "the", "most", "medicines", "as", "an", "activator", "?" ]
[ { "id": 3, "type": "table", "value": "medicine_enzyme_interaction" }, { "id": 4, "type": "column", "value": "interaction_type" }, { "id": 5, "type": "value", "value": "activitor" }, { "id": 6, "type": "column", "value": "enzyme_id" }, { "id": 2, "type": "table", "value": "enzyme" }, { "id": 1, "type": "column", "value": "name" }, { "id": 0, "type": "column", "value": "id" } ]
[ { "entity_id": 0, "token_idxs": [ 3, 4 ] }, { "entity_id": 1, "token_idxs": [ 6 ] }, { "entity_id": 2, "token_idxs": [ 9 ] }, { "entity_id": 3, "token_idxs": [ 16 ] }, { "entity_id": 4, "token_idxs": [ 12 ] }, { "entity_id": 5, "token_idxs": [ 19 ] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "B-COLUMN", "O", "O", "O", "B-TABLE", "O", "O", "B-VALUE", "O" ]
90
cre_Doc_Tracking_DB
spider:train_spider.json:4234
What are the names of the employees who authorised the destruction and the employees who destroyed the corresponding documents?
SELECT T2.employee_name , T3.employee_name FROM Documents_to_be_destroyed AS T1 JOIN Employees AS T2 ON T1.Destruction_Authorised_by_Employee_ID = T2.employee_id JOIN Employees AS T3 ON T1.Destroyed_by_Employee_ID = T3.employee_id;
[ "What", "are", "the", "names", "of", "the", "employees", "who", "authorised", "the", "destruction", "and", "the", "employees", "who", "destroyed", "the", "corresponding", "documents", "?" ]
[ { "id": 5, "type": "column", "value": "destruction_authorised_by_employee_id" }, { "id": 2, "type": "table", "value": "documents_to_be_destroyed" }, { "id": 3, "type": "column", "value": "destroyed_by_employee_id" }, { "id": 0, "type": "column", "value": "employee_name" }, { "id": 4, "type": "column", "value": "employee_id" }, { "id": 1, "type": "table", "value": "employees" } ]
[ { "entity_id": 0, "token_idxs": [ 3 ] }, { "entity_id": 1, "token_idxs": [ 6 ] }, { "entity_id": 2, "token_idxs": [ 18 ] }, { "entity_id": 3, "token_idxs": [ 13, 14, 15 ] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [ 10 ] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "B-COLUMN", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "O", "B-TABLE", "O" ]
960
superhero
bird:dev.json:812
List down at least five full names of superheroes with blue eyes.
SELECT T1.superhero_name FROM superhero AS T1 INNER JOIN colour AS T2 ON T1.eye_colour_id = T2.id WHERE T2.colour = 'Blue' LIMIT 5
[ "List", "down", "at", "least", "five", "full", "names", "of", "superheroes", "with", "blue", "eyes", "." ]
[ { "id": 0, "type": "column", "value": "superhero_name" }, { "id": 5, "type": "column", "value": "eye_colour_id" }, { "id": 1, "type": "table", "value": "superhero" }, { "id": 2, "type": "table", "value": "colour" }, { "id": 3, "type": "column", "value": "colour" }, { "id": 4, "type": "value", "value": "Blue" }, { "id": 6, "type": "column", "value": "id" } ]
[ { "entity_id": 0, "token_idxs": [ 6 ] }, { "entity_id": 1, "token_idxs": [ 8 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [ 10 ] }, { "entity_id": 5, "token_idxs": [ 11 ] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "B-VALUE", "B-COLUMN", "O" ]
6,227
student_club
bird:dev.json:1384
How many income generated by Grant Gilmour?
SELECT T2.amount FROM member AS T1 INNER JOIN income AS T2 ON T1.member_id = T2.link_to_member WHERE T1.first_name = 'Grant' AND T1.last_name = 'Gilmour'
[ "How", "many", "income", "generated", "by", "Grant", "Gilmour", "?" ]
[ { "id": 4, "type": "column", "value": "link_to_member" }, { "id": 5, "type": "column", "value": "first_name" }, { "id": 3, "type": "column", "value": "member_id" }, { "id": 7, "type": "column", "value": "last_name" }, { "id": 8, "type": "value", "value": "Gilmour" }, { "id": 0, "type": "column", "value": "amount" }, { "id": 1, "type": "table", "value": "member" }, { "id": 2, "type": "table", "value": "income" }, { "id": 6, "type": "value", "value": "Grant" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 2 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [ 5 ] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [ 6 ] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-TABLE", "O", "O", "B-VALUE", "B-VALUE", "O" ]
9,432
chinook_1
spider:train_spider.json:857
Find all the customer last names that do not have invoice totals larger than 20.
SELECT LastName FROM CUSTOMER EXCEPT SELECT T1.LastName FROM CUSTOMER AS T1 JOIN Invoice AS T2 ON T1.CustomerId = T2.CustomerId WHERE T2.total > 20
[ "Find", "all", "the", "customer", "last", "names", "that", "do", "not", "have", "invoice", "totals", "larger", "than", "20", "." ]
[ { "id": 5, "type": "column", "value": "customerid" }, { "id": 0, "type": "table", "value": "customer" }, { "id": 1, "type": "column", "value": "lastname" }, { "id": 2, "type": "table", "value": "invoice" }, { "id": 3, "type": "column", "value": "total" }, { "id": 4, "type": "value", "value": "20" } ]
[ { "entity_id": 0, "token_idxs": [ 3 ] }, { "entity_id": 1, "token_idxs": [ 4, 5 ] }, { "entity_id": 2, "token_idxs": [ 10 ] }, { "entity_id": 3, "token_idxs": [ 11 ] }, { "entity_id": 4, "token_idxs": [ 14 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-TABLE", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O", "B-TABLE", "B-COLUMN", "O", "O", "B-VALUE", "O" ]
10,177
boat_1
bird:test.json:904
What is the name, rating, and age for every sailor? And order them by rating and age.
SELECT name , rating , age FROM Sailors ORDER BY rating , age
[ "What", "is", "the", "name", ",", "rating", ",", "and", "age", "for", "every", "sailor", "?", "And", "order", "them", "by", "rating", "and", "age", "." ]
[ { "id": 0, "type": "table", "value": "sailors" }, { "id": 2, "type": "column", "value": "rating" }, { "id": 1, "type": "column", "value": "name" }, { "id": 3, "type": "column", "value": "age" } ]
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[ "O", "O", "B-COLUMN", "O", "O", "O", "O", "B-TABLE", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O" ]
6,615
simpson_episodes
bird:train.json:4323
Which title is the winner of Best International TV Series in 2017?
SELECT T2.title FROM Award AS T1 INNER JOIN Episode AS T2 ON T1.episode_id = T2.episode_id WHERE T1.award = 'Best International TV Series' AND SUBSTR(T1.year, 1, 4) = '2017';
[ "Which", "title", "is", "the", "winner", "of", "Best", "International", "TV", "Series", "in", "2017", "?" ]
[ { "id": 5, "type": "value", "value": "Best International TV Series" }, { "id": 3, "type": "column", "value": "episode_id" }, { "id": 2, "type": "table", "value": "episode" }, { "id": 0, "type": "column", "value": "title" }, { "id": 1, "type": "table", "value": "award" }, { "id": 4, "type": "column", "value": "award" }, { "id": 6, "type": "value", "value": "2017" }, { "id": 7, "type": "column", "value": "year" }, { "id": 8, "type": "value", "value": "1" }, { "id": 9, "type": "value", "value": "4" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "B-TABLE", "O", "O", "O", "O", "O", "O", "O" ]
13,218
works_cycles
bird:train.json:7295
Please list the website purchasing links of the vendors from whom the product Hex Nut 5 can be purchased.
SELECT T3.PurchasingWebServiceURL FROM ProductVendor AS T1 INNER JOIN Product AS T2 ON T1.ProductID = T2.ProductID INNER JOIN Vendor AS T3 ON T1.BusinessEntityID = T3.BusinessEntityID WHERE T2.Name = 'Hex Nut 5'
[ "Please", "list", "the", "website", "purchasing", "links", "of", "the", "vendors", "from", "whom", "the", "product", "Hex", "Nut", "5", "can", "be", "purchased", "." ]
[ { "id": 0, "type": "column", "value": "purchasingwebserviceurl" }, { "id": 6, "type": "column", "value": "businessentityid" }, { "id": 4, "type": "table", "value": "productvendor" }, { "id": 3, "type": "value", "value": "Hex Nut 5" }, { "id": 7, "type": "column", "value": "productid" }, { "id": 5, "type": "table", "value": "product" }, { "id": 1, "type": "table", "value": "vendor" }, { "id": 2, "type": "column", "value": "name" } ]
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[ "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-TABLE", "O", "O", "O", "B-TABLE", "B-VALUE", "I-VALUE", "I-VALUE", "O", "O", "O", "O" ]
13,443
tracking_orders
spider:train_spider.json:6942
What are the names of the customers who bought product "food" at least once?
SELECT T1.customer_name FROM customers AS T1 JOIN orders AS T2 JOIN order_items AS T3 JOIN products AS T4 ON T1.customer_id = T2.customer_id AND T2.order_id = T3.order_id AND T3.product_id = T4.product_id WHERE T4.product_name = "food" GROUP BY T1.customer_id HAVING count(*) >= 1
[ "What", "are", "the", "names", "of", "the", "customers", "who", "bought", "product", "\"", "food", "\"", "at", "least", "once", "?" ]
[]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O" ]
7,752
ice_hockey_draft
bird:train.json:6952
How many players who were drafted by the Toronto Maple Leafs have played over 300 games in their first 7 years of the NHL career?
SELECT COUNT(ELITEID) FROM PlayerInfo WHERE overallby = 'Toronto Maple Leafs' AND sum_7yr_GP > 300
[ "How", "many", "players", "who", "were", "drafted", "by", "the", "Toronto", "Maple", "Leafs", "have", "played", "over", "300", "games", "in", "their", "first", "7", "years", "of", "the", "NHL", "career", "?" ]
[ { "id": 3, "type": "value", "value": "Toronto Maple Leafs" }, { "id": 0, "type": "table", "value": "playerinfo" }, { "id": 4, "type": "column", "value": "sum_7yr_gp" }, { "id": 2, "type": "column", "value": "overallby" }, { "id": 1, "type": "column", "value": "eliteid" }, { "id": 5, "type": "value", "value": "300" } ]
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[ "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "B-VALUE", "I-VALUE", "I-VALUE", "O", "O", "B-COLUMN", "B-VALUE", "O", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "O", "O", "O", "O" ]
8,346
machine_repair
spider:train_spider.json:2241
Show the name of technicians aged either 36 or 37
SELECT Name FROM technician WHERE Age = 36 OR Age = 37
[ "Show", "the", "name", "of", "technicians", "aged", "either", "36", "or", "37" ]
[ { "id": 0, "type": "table", "value": "technician" }, { "id": 1, "type": "column", "value": "name" }, { "id": 2, "type": "column", "value": "age" }, { "id": 3, "type": "value", "value": "36" }, { "id": 4, "type": "value", "value": "37" } ]
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[ "O", "O", "B-COLUMN", "O", "B-TABLE", "B-COLUMN", "O", "B-VALUE", "O", "B-VALUE" ]
916
tracking_software_problems
spider:train_spider.json:5356
Find all the ids and dates of the logs for the problem whose id is 10.
SELECT problem_log_id , log_entry_date FROM problem_log WHERE problem_id = 10
[ "Find", "all", "the", "ids", "and", "dates", "of", "the", "logs", "for", "the", "problem", "whose", "i", "d", "is", "10", "." ]
[ { "id": 1, "type": "column", "value": "problem_log_id" }, { "id": 2, "type": "column", "value": "log_entry_date" }, { "id": 0, "type": "table", "value": "problem_log" }, { "id": 3, "type": "column", "value": "problem_id" }, { "id": 4, "type": "value", "value": "10" } ]
[ { "entity_id": 0, "token_idxs": [ 8 ] }, { "entity_id": 1, "token_idxs": [ 3 ] }, { "entity_id": 2, "token_idxs": [ 5 ] }, { "entity_id": 3, "token_idxs": [ 11, 13, 14 ] }, { "entity_id": 4, "token_idxs": [ 16 ] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O", "B-TABLE", "O", "O", "B-COLUMN", "O", "B-COLUMN", "I-COLUMN", "O", "B-VALUE", "O" ]
2,608
superstore
bird:train.json:2357
What was the original price of Xerox 1952 ordered by Aimee Bixby on 2014/9/10?
SELECT DISTINCT T2.Sales / (1 - T2.Discount) FROM people AS T1 INNER JOIN central_superstore AS T2 ON T1.`Customer ID` = T2.`Customer ID` INNER JOIN product AS T3 ON T3.`Product ID` = T2.`Product ID` WHERE T1.`Customer Name` = 'Aimee Bixby' AND T3.`Product Name` = 'Xerox 1952' AND T2.`Order Date` = '2014-09-10'
[ "What", "was", "the", "original", "price", "of", "Xerox", "1952", "ordered", "by", "Aimee", "Bixby", "on", "2014/9/10", "?" ]
[ { "id": 3, "type": "table", "value": "central_superstore" }, { "id": 5, "type": "column", "value": "Customer Name" }, { "id": 7, "type": "column", "value": "Product Name" }, { "id": 6, "type": "value", "value": "Aimee Bixby" }, { "id": 13, "type": "column", "value": "Customer ID" }, { "id": 4, "type": "column", "value": "Product ID" }, { "id": 8, "type": "value", "value": "Xerox 1952" }, { "id": 9, "type": "column", "value": "Order Date" }, { "id": 10, "type": "value", "value": "2014-09-10" }, { "id": 12, "type": "column", "value": "discount" }, { "id": 0, "type": "table", "value": "product" }, { "id": 2, "type": "table", "value": "people" }, { "id": 1, "type": "column", "value": "sales" }, { "id": 11, "type": "value", "value": "1" } ]
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[ "O", "O", "O", "O", "O", "O", "B-VALUE", "I-VALUE", "B-COLUMN", "O", "B-VALUE", "I-VALUE", "O", "B-VALUE", "O" ]
12,272
hr_1
spider:train_spider.json:3447
display the employee ID for each employee and the date on which he ended his previous job.
SELECT employee_id , MAX(end_date) FROM job_history GROUP BY employee_id
[ "display", "the", "employee", "ID", "for", "each", "employee", "and", "the", "date", "on", "which", "he", "ended", "his", "previous", "job", "." ]
[ { "id": 0, "type": "table", "value": "job_history" }, { "id": 1, "type": "column", "value": "employee_id" }, { "id": 2, "type": "column", "value": "end_date" } ]
[ { "entity_id": 0, "token_idxs": [ 16 ] }, { "entity_id": 1, "token_idxs": [ 2, 3 ] }, { "entity_id": 2, "token_idxs": [ 9 ] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "B-TABLE", "O" ]
1,588
toxicology
bird:dev.json:270
Among the molecules with element Calcium, are they mostly carcinogenic or non carcinogenic?
SELECT T2.label FROM atom AS T1 INNER JOIN molecule AS T2 ON T1.molecule_id = T2.molecule_id WHERE T1.element = 'ca' GROUP BY T2.label ORDER BY COUNT(T2.label) DESC LIMIT 1
[ "Among", "the", "molecules", "with", "element", "Calcium", ",", "are", "they", "mostly", "carcinogenic", "or", "non", "carcinogenic", "?" ]
[ { "id": 5, "type": "column", "value": "molecule_id" }, { "id": 2, "type": "table", "value": "molecule" }, { "id": 3, "type": "column", "value": "element" }, { "id": 0, "type": "column", "value": "label" }, { "id": 1, "type": "table", "value": "atom" }, { "id": 4, "type": "value", "value": "ca" } ]
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[ "O", "O", "B-TABLE", "O", "B-COLUMN", "B-VALUE", "O", "O", "O", "O", "O", "O", "O", "O", "O" ]
3,608
book_publishing_company
bird:train.json:184
List all employees who are at the maximum level in their job designation.
SELECT T1.fname, T1.lname FROM employee AS T1 INNER JOIN jobs AS T2 ON T1.job_id = T2.job_id WHERE T1.job_lvl = T2.max_lvl
[ "List", "all", "employees", "who", "are", "at", "the", "maximum", "level", "in", "their", "job", "designation", "." ]
[ { "id": 2, "type": "table", "value": "employee" }, { "id": 4, "type": "column", "value": "job_lvl" }, { "id": 5, "type": "column", "value": "max_lvl" }, { "id": 6, "type": "column", "value": "job_id" }, { "id": 0, "type": "column", "value": "fname" }, { "id": 1, "type": "column", "value": "lname" }, { "id": 3, "type": "table", "value": "jobs" } ]
[ { "entity_id": 0, "token_idxs": [] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [ 2 ] }, { "entity_id": 3, "token_idxs": [ 11 ] }, { "entity_id": 4, "token_idxs": [ 8 ] }, { "entity_id": 5, "token_idxs": [ 7 ] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-TABLE", "O", "O", "O", "O", "B-COLUMN", "B-COLUMN", "O", "O", "B-TABLE", "O", "O" ]
4,386
soccer_2016
bird:train.json:1903
Which player became the man of the series in the year 2012? Give the name and country of this player.
SELECT T2.Player_Name, T3.Country_Name FROM Season AS T1 INNER JOIN Player AS T2 ON T1.Man_of_the_Series = T2.Player_Id INNER JOIN Country AS T3 ON T2.Country_Name = T3.Country_Id WHERE T1.Season_Year = 2012
[ "Which", "player", "became", "the", "man", "of", "the", "series", "in", "the", "year", "2012", "?", "Give", "the", "name", "and", "country", "of", "this", "player", "." ]
[ { "id": 8, "type": "column", "value": "man_of_the_series" }, { "id": 1, "type": "column", "value": "country_name" }, { "id": 0, "type": "column", "value": "player_name" }, { "id": 3, "type": "column", "value": "season_year" }, { "id": 7, "type": "column", "value": "country_id" }, { "id": 9, "type": "column", "value": "player_id" }, { "id": 2, "type": "table", "value": "country" }, { "id": 5, "type": "table", "value": "season" }, { "id": 6, "type": "table", "value": "player" }, { "id": 4, "type": "value", "value": "2012" } ]
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[ "O", "B-TABLE", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "I-COLUMN", "O", "O", "B-COLUMN", "B-VALUE", "O", "O", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O", "B-COLUMN", "O" ]
14,013
hr_1
spider:train_spider.json:3511
display the department id and the total salary for those departments which contains at least two employees.
SELECT department_id , SUM(salary) FROM employees GROUP BY department_id HAVING count(*) >= 2
[ "display", "the", "department", "i", "d", "and", "the", "total", "salary", "for", "those", "departments", "which", "contains", "at", "least", "two", "employees", "." ]
[ { "id": 1, "type": "column", "value": "department_id" }, { "id": 0, "type": "table", "value": "employees" }, { "id": 3, "type": "column", "value": "salary" }, { "id": 2, "type": "value", "value": "2" } ]
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[ "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "O", "O", "O", "B-VALUE", "B-TABLE", "O" ]
8,834
superhero
bird:dev.json:725
How many superheroes are published by Marvel Comics?
SELECT COUNT(T1.id) FROM superhero AS T1 INNER JOIN publisher AS T2 ON T1.publisher_id = T2.id WHERE T2.publisher_name = 'Marvel Comics'
[ "How", "many", "superheroes", "are", "published", "by", "Marvel", "Comics", "?" ]
[ { "id": 2, "type": "column", "value": "publisher_name" }, { "id": 3, "type": "value", "value": "Marvel Comics" }, { "id": 5, "type": "column", "value": "publisher_id" }, { "id": 0, "type": "table", "value": "superhero" }, { "id": 1, "type": "table", "value": "publisher" }, { "id": 4, "type": "column", "value": "id" } ]
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[ "O", "O", "B-TABLE", "O", "B-TABLE", "O", "B-VALUE", "I-VALUE", "O" ]
4,351
legislator
bird:train.json:4840
Provide the full name and birth date of the legislator with a contact form of http://www.brown.senate.gov/contact/.
SELECT T1.official_full_name, T1.birthday_bio FROM current AS T1 INNER JOIN `current-terms` AS T2 ON T1.bioguide_id = T2.bioguide WHERE T2.contact_form = 'http://www.brown.senate.gov/contact/'
[ "Provide", "the", "full", "name", "and", "birth", "date", "of", "the", "legislator", "with", "a", "contact", "form", "of", "http://www.brown.senate.gov/contact/." ]
[ { "id": 5, "type": "value", "value": "http://www.brown.senate.gov/contact/" }, { "id": 0, "type": "column", "value": "official_full_name" }, { "id": 3, "type": "table", "value": "current-terms" }, { "id": 1, "type": "column", "value": "birthday_bio" }, { "id": 4, "type": "column", "value": "contact_form" }, { "id": 6, "type": "column", "value": "bioguide_id" }, { "id": 7, "type": "column", "value": "bioguide" }, { "id": 2, "type": "table", "value": "current" } ]
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[ "O", "O", "B-COLUMN", "O", "O", "O", "O", "B-COLUMN", "B-TABLE", "O", "O", "O", "O", "O", "O", "O" ]
15,848
shipping
bird:train.json:5592
State the headquarter of the truck which completed shipment no.1045.
SELECT T1.make FROM truck AS T1 INNER JOIN shipment AS T2 ON T1.truck_id = T2.truck_id WHERE T2.ship_id = 1045
[ "State", "the", "headquarter", "of", "the", "truck", "which", "completed", "shipment", "no.1045", "." ]
[ { "id": 2, "type": "table", "value": "shipment" }, { "id": 5, "type": "column", "value": "truck_id" }, { "id": 3, "type": "column", "value": "ship_id" }, { "id": 1, "type": "table", "value": "truck" }, { "id": 0, "type": "column", "value": "make" }, { "id": 4, "type": "value", "value": "1045" } ]
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[ "B-COLUMN", "O", "O", "O", "O", "B-TABLE", "O", "O", "B-TABLE", "B-VALUE", "O" ]
13,353
boat_1
bird:test.json:900
How many reservations for each boat did the sailors with an id greater than 1 make?
SELECT bid , count(*) FROM Reserves WHERE sid > 1 GROUP BY bid
[ "How", "many", "reservations", "for", "each", "boat", "did", "the", "sailors", "with", "an", "i", "d", "greater", "than", "1", "make", "?" ]
[ { "id": 0, "type": "table", "value": "reserves" }, { "id": 1, "type": "column", "value": "bid" }, { "id": 2, "type": "column", "value": "sid" }, { "id": 3, "type": "value", "value": "1" } ]
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[ "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-VALUE", "O", "O" ]
6,275
movie_3
bird:train.json:9291
Which city does the address 1623 Kingstown Drive belong to?
SELECT T1.city FROM city AS T1 INNER JOIN address AS T2 ON T2.city_id = T1.city_id WHERE T2.address = '1623 Kingstown Drive'
[ "Which", "city", "does", "the", "address", "1623", "Kingstown", "Drive", "belong", "to", "?" ]
[ { "id": 4, "type": "value", "value": "1623 Kingstown Drive" }, { "id": 2, "type": "table", "value": "address" }, { "id": 3, "type": "column", "value": "address" }, { "id": 5, "type": "column", "value": "city_id" }, { "id": 0, "type": "column", "value": "city" }, { "id": 1, "type": "table", "value": "city" } ]
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[ "O", "B-TABLE", "O", "O", "B-COLUMN", "B-VALUE", "I-VALUE", "I-VALUE", "O", "O", "O" ]
15,126
movie_platform
bird:train.json:126
How many critics were given to the movie that got the most movie popularity number.
SELECT COUNT(T1.critic) FROM ratings AS T1 INNER JOIN movies AS T2 ON T1.movie_id = T2.movie_id WHERE T2.movie_popularity = ( SELECT MAX(movie_popularity) FROM movies )
[ "How", "many", "critics", "were", "given", "to", "the", "movie", "that", "got", "the", "most", "movie", "popularity", "number", "." ]
[ { "id": 2, "type": "column", "value": "movie_popularity" }, { "id": 4, "type": "column", "value": "movie_id" }, { "id": 0, "type": "table", "value": "ratings" }, { "id": 1, "type": "table", "value": "movies" }, { "id": 3, "type": "column", "value": "critic" } ]
[ { "entity_id": 0, "token_idxs": [ 2 ] }, { "entity_id": 1, "token_idxs": [] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O" ]
10,280
music_2
spider:train_spider.json:5223
What is the type of vocals that the band member with the last name "Heilo" played the most?
SELECT TYPE FROM vocals AS T1 JOIN band AS T2 ON T1.bandmate = T2.id WHERE lastname = "Heilo" GROUP BY TYPE ORDER BY count(*) DESC LIMIT 1
[ "What", "is", "the", "type", "of", "vocals", "that", "the", "band", "member", "with", "the", "last", "name", "\"", "Heilo", "\"", "played", "the", "most", "?" ]
[ { "id": 3, "type": "column", "value": "lastname" }, { "id": 5, "type": "column", "value": "bandmate" }, { "id": 1, "type": "table", "value": "vocals" }, { "id": 4, "type": "column", "value": "Heilo" }, { "id": 0, "type": "column", "value": "type" }, { "id": 2, "type": "table", "value": "band" }, { "id": 6, "type": "column", "value": "id" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "O", "B-TABLE", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "B-COLUMN", "O", "O", "O", "O", "O" ]
3,257
planet_1
bird:test.json:1929
Find the number of employees who do not have clearance in Mars .
select count(*) from employee where employeeid not in ( select t2.employeeid from has_clearance as t1 join employee as t2 on t1.employee = t2.employeeid join planet as t3 on t1.planet = t3.planetid where t3.name = "mars" );
[ "Find", "the", "number", "of", "employees", "who", "do", "not", "have", "clearance", "in", "Mars", "." ]
[ { "id": 5, "type": "table", "value": "has_clearance" }, { "id": 1, "type": "column", "value": "employeeid" }, { "id": 0, "type": "table", "value": "employee" }, { "id": 7, "type": "column", "value": "planetid" }, { "id": 8, "type": "column", "value": "employee" }, { "id": 2, "type": "table", "value": "planet" }, { "id": 6, "type": "column", "value": "planet" }, { "id": 3, "type": "column", "value": "name" }, { "id": 4, "type": "column", "value": "mars" } ]
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[ "O", "O", "O", "O", "B-TABLE", "O", "O", "O", "B-TABLE", "I-TABLE", "O", "B-COLUMN", "O" ]
14,153
soccer_2016
bird:train.json:1826
How many umpires are from South Africa?
SELECT SUM(CASE WHEN T1.Country_Name = 'South Africa' THEN 1 ELSE 0 END) FROM Country AS T1 INNER JOIN Umpire AS T2 ON T1.Country_ID = T2.Umpire_Country
[ "How", "many", "umpires", "are", "from", "South", "Africa", "?" ]
[ { "id": 3, "type": "column", "value": "umpire_country" }, { "id": 6, "type": "column", "value": "country_name" }, { "id": 7, "type": "value", "value": "South Africa" }, { "id": 2, "type": "column", "value": "country_id" }, { "id": 0, "type": "table", "value": "country" }, { "id": 1, "type": "table", "value": "umpire" }, { "id": 4, "type": "value", "value": "0" }, { "id": 5, "type": "value", "value": "1" } ]
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[ "O", "O", "B-TABLE", "O", "O", "B-VALUE", "I-VALUE", "O" ]
5,718
donor
bird:train.json:3244
For the donation of the project 'Awesome Audiobooks Make Avid Readers', what was the percentage of the tip in the total amount?
SELECT CAST(SUM(T2.donation_optional_support) AS REAL) * 100 / SUM(T2.donation_total) FROM essays AS T1 INNER JOIN donations AS T2 ON T1.projectid = T2.projectid WHERE T1.title LIKE 'Awesome Audiobooks Make Avid Readers'
[ "For", "the", "donation", "of", "the", "project", "'", "Awesome", "Audiobooks", "Make", "Avid", "Readers", "'", ",", "what", "was", "the", "percentage", "of", "the", "tip", "in", "the", "total", "amount", "?" ]
[ { "id": 3, "type": "value", "value": "Awesome Audiobooks Make Avid Readers" }, { "id": 7, "type": "column", "value": "donation_optional_support" }, { "id": 6, "type": "column", "value": "donation_total" }, { "id": 1, "type": "table", "value": "donations" }, { "id": 4, "type": "column", "value": "projectid" }, { "id": 0, "type": "table", "value": "essays" }, { "id": 2, "type": "column", "value": "title" }, { "id": 5, "type": "value", "value": "100" } ]
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[ "O", "O", "O", "B-TABLE", "O", "B-COLUMN", "O", "B-TABLE", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O" ]
3,910
flight_4
spider:train_spider.json:6831
Find the name, city, and country of the airport that has the highest latitude.
SELECT name , city , country FROM airports ORDER BY elevation DESC LIMIT 1
[ "Find", "the", "name", ",", "city", ",", "and", "country", "of", "the", "airport", "that", "has", "the", "highest", "latitude", "." ]
[ { "id": 4, "type": "column", "value": "elevation" }, { "id": 0, "type": "table", "value": "airports" }, { "id": 3, "type": "column", "value": "country" }, { "id": 1, "type": "column", "value": "name" }, { "id": 2, "type": "column", "value": "city" } ]
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[ "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "O", "O", "O", "O", "O" ]
13,105
region_building
bird:test.json:326
List the names of regions in alphabetical order.
SELECT Name FROM region ORDER BY Name ASC
[ "List", "the", "names", "of", "regions", "in", "alphabetical", "order", "." ]
[ { "id": 0, "type": "table", "value": "region" }, { "id": 1, "type": "column", "value": "name" } ]
[ { "entity_id": 0, "token_idxs": [ 4 ] }, { "entity_id": 1, "token_idxs": [ 2 ] }, { "entity_id": 2, "token_idxs": [] }, { "entity_id": 3, "token_idxs": [] }, { "entity_id": 4, "token_idxs": [] }, { "entity_id": 5, "token_idxs": [] }, { "entity_id": 6, "token_idxs": [] }, { "entity_id": 7, "token_idxs": [] }, { "entity_id": 8, "token_idxs": [] }, { "entity_id": 9, "token_idxs": [] }, { "entity_id": 10, "token_idxs": [] }, { "entity_id": 11, "token_idxs": [] }, { "entity_id": 12, "token_idxs": [] }, { "entity_id": 13, "token_idxs": [] }, { "entity_id": 14, "token_idxs": [] }, { "entity_id": 15, "token_idxs": [] }, { "entity_id": 16, "token_idxs": [] }, { "entity_id": 17, "token_idxs": [] }, { "entity_id": 18, "token_idxs": [] }, { "entity_id": 19, "token_idxs": [] } ]
[ "O", "O", "B-COLUMN", "O", "B-TABLE", "O", "O", "O", "O" ]
14,737
shooting
bird:train.json:2469
From the cases where the subject are male, list the case number and the location and subject status.
SELECT T1.case_number, T1.location, T1.subject_statuses FROM incidents AS T1 INNER JOIN subjects AS T2 ON T1.case_number = T2.case_number WHERE T2.gender = 'M'
[ "From", "the", "cases", "where", "the", "subject", "are", "male", ",", "list", "the", "case", "number", "and", "the", "location", "and", "subject", "status", "." ]
[ { "id": 2, "type": "column", "value": "subject_statuses" }, { "id": 0, "type": "column", "value": "case_number" }, { "id": 3, "type": "table", "value": "incidents" }, { "id": 1, "type": "column", "value": "location" }, { "id": 4, "type": "table", "value": "subjects" }, { "id": 5, "type": "column", "value": "gender" }, { "id": 6, "type": "value", "value": "M" } ]
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[ "O", "O", "O", "O", "O", "O", "O", "B-VALUE", "O", "O", "O", "B-COLUMN", "I-COLUMN", "O", "O", "B-COLUMN", "O", "B-TABLE", "B-COLUMN", "O" ]
5,514
synthea
bird:train.json:1420
List the procedures received by Emmy Waelchi.
SELECT T2.DESCRIPTION FROM patients AS T1 INNER JOIN procedures AS T2 ON T1.patient = T2.PATIENT WHERE T1.first = 'Emmy' AND T1.last = 'Waelchi'
[ "List", "the", "procedures", "received", "by", "Emmy", "Waelchi", "." ]
[ { "id": 0, "type": "column", "value": "description" }, { "id": 2, "type": "table", "value": "procedures" }, { "id": 1, "type": "table", "value": "patients" }, { "id": 3, "type": "column", "value": "patient" }, { "id": 7, "type": "value", "value": "Waelchi" }, { "id": 4, "type": "column", "value": "first" }, { "id": 5, "type": "value", "value": "Emmy" }, { "id": 6, "type": "column", "value": "last" } ]
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[ "B-COLUMN", "O", "B-TABLE", "O", "O", "B-VALUE", "B-VALUE", "O" ]
53
cre_Drama_Workshop_Groups
spider:train_spider.json:5162
What are the names of the workshop groups that have bookings with status code "stop"?
SELECT T2.Store_Name FROM Bookings AS T1 JOIN Drama_Workshop_Groups AS T2 ON T1.Workshop_Group_ID = T2.Workshop_Group_ID WHERE T1.Status_Code = "stop"
[ "What", "are", "the", "names", "of", "the", "workshop", "groups", "that", "have", "bookings", "with", "status", "code", "\"", "stop", "\"", "?" ]
[ { "id": 2, "type": "table", "value": "drama_workshop_groups" }, { "id": 5, "type": "column", "value": "workshop_group_id" }, { "id": 3, "type": "column", "value": "status_code" }, { "id": 0, "type": "column", "value": "store_name" }, { "id": 1, "type": "table", "value": "bookings" }, { "id": 4, "type": "column", "value": "stop" } ]
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[ "O", "O", "O", "B-COLUMN", "O", "O", "O", "O", "B-COLUMN", "O", "B-COLUMN", "O", "O", "O", "O", "O", "B-TABLE", "I-TABLE" ]
9,016
retail_world
bird:train.json:6617
Which products by Plutzer Lebensmittelgromrkte AG were discontinued and what are their price?
SELECT T1.UnitPrice FROM Products AS T1 INNER JOIN Suppliers AS T2 ON T1.SupplierID = T2.SupplierID WHERE T2.CompanyName = 'Plutzer Lebensmittelgromrkte AG' AND T1.Discontinued = 1
[ "Which", "products", "by", "Plutzer", "Lebensmittelgromrkte", "AG", "were", "discontinued", "and", "what", "are", "their", "price", "?" ]
[ { "id": 5, "type": "value", "value": "Plutzer Lebensmittelgromrkte AG" }, { "id": 6, "type": "column", "value": "discontinued" }, { "id": 4, "type": "column", "value": "companyname" }, { "id": 3, "type": "column", "value": "supplierid" }, { "id": 0, "type": "column", "value": "unitprice" }, { "id": 2, "type": "table", "value": "suppliers" }, { "id": 1, "type": "table", "value": "products" }, { "id": 7, "type": "value", "value": "1" } ]
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[ "O", "O", "B-TABLE", "O", "O", "O", "O", "B-COLUMN", "I-COLUMN", "I-COLUMN", "I-COLUMN", "B-COLUMN", "O", "O" ]