  We present a novel method for injecting temporality into entailment graphs to
address the problem of spurious entailments, which may arise from similar but
temporally distinct events involving the same pair of entities. We focus on the
sports domain in which the same pairs of teams play on different occasions,
with different outcomes. We present an unsupervised model that aims to learn
entailments such as win/lose $\rightarrow$ play, while avoiding the pitfall of
learning non-entailments such as win $\not\rightarrow$ lose. We evaluate our
model on a manually constructed dataset, showing that incorporating time
intervals and applying a temporal window around them, are effective strategies.
