It is trained on the (text-level) "instances" of relatively common names (appearing more than 100 times in the corpus) from the 3 NE label types derived based on the Brown clustering.
to build a training set, we pass through the corpus and each time we come across a common name in a particular document, we build a feature vector corresponding to all the contexts in that document, with the label taken from the clustering.