  We introduce CHEW, a novel dataset of changing events in Wikipedia expressed
in naturally occurring text. We use CHEW for probing LLMs for their timeline
understanding of Wikipedia entities and events in generative and classification
experiments. Our results suggest that LLMs, despite having temporal information
available, struggle to construct accurate timelines. We further show the
usefulness of CHEW-derived embeddings for identifying meaning shift.
