The second point was confirmed by providing the ideal (100% correct) output from the (forthcoming) NLP components to a state-of-theart automatic reasoner and comparing the result against the performance of the reasoner on the input formulated by a human expert.
we manually gave the semantic representations of the problems as iDRSs or CCG derivation trees, and then automatically rewrote them into the language of RCF.