Understanding anomalies in real-world videos requires reasoning about when events occur, where they happen, and what actions unfold. Existing benchmarks, however, largely ignore one or more of these dimensions, making it difficult to evaluate whether multimodal large models (MLLMs) truly understand abnormal events beyond simple detection. To address this gap, we introduce Anom3W, a benchmark designed to evaluate MLLMs across temporal, spatial, and semantic dimensions. Our semi-automatic pipeline consolidates heterogeneous anomaly resources into verified spatio-temporal annotations, enabling rigorous, reasoning-oriented evaluation. By treating spatial boxes and tubes as evidence cues, Anom3W challenges models to reason about entities, locations, and behaviors, rather than merely flag anomalies. Experiments demonstrate that even state-of-the-art MLLMs struggle, highlighting the critical importance of spatial grounding and semantic understanding. Anom3W establishes a new standard for interpretable, context-aware video anomaly reasoning.