QA TempEval shifts the goal of previous TempEvals away from an intrinsic evaluation methodology toward a more extrinsic goal of question answering. This evaluation requires systems to capture temporal information relevant to perform an end-user task, as opposed to corpus-based evaluation where all temporal information is equally important. Evaluation results show that the best automated TimeML annotations reach over 30% recall on questions with ‘yes’ answer and about 50% on easier questions with ‘no’ answers. Features that helped achieve better results are event coreference and a time expression reasoner.