Imagine a digital historian, meticulously tracking every edit, every update, every subtle shift in information across the vast expanse of Wikipedia. That's the essence of CHEW, a new dataset designed to capture the evolving narrative of events as reflected in Wikipedia's ever-changing pages. CHEW isn't just about archiving revisions; it's about understanding how information morphs over time. This dataset focuses on identifying significant changes to events and entities, filtering out minor edits and stylistic tweaks to pinpoint moments of genuine informational evolution. Researchers are using CHEW to test the "timeline awareness" of Large Language Models (LLMs). Can these AI behemoths accurately reconstruct the historical progression of events based on Wikipedia's edits? Early experiments reveal a fascinating challenge: while LLMs can access temporal data, weaving it into a coherent timeline proves surprisingly difficult. This isn't merely an academic exercise. Understanding how LLMs process temporal information has significant real-world implications. Imagine AI fact-checkers that can instantly verify claims against a historical record, or personalized news feeds that adapt to evolving events in real-time. However, there are hurdles to overcome. The current research primarily focuses on English Wikipedia, limiting its scope. Expanding to other languages and ensuring accuracy in identifying meaningful changes remain key challenges. But the journey has begun. With datasets like CHEW, we're not just building better AI; we're building AI that can understand the unfolding story of our world.