This paper demonstrates how a model for temporal context reasoning can be implemented.
The approach is to detect temporally related events in natural language text and convert the
events into an enriched logical representation. Reasoning is provided by a first order logic
theorem prover adapted to text. Results show that temporal context reasoning boosts the
performance of a Question Answering system.