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

Incorporating Temporal Information in Entailment Graph Mining