Accurate reasoning about time-sensitive facts is essential in today's rapidly evolving knowledge landscape. While Large Language Models (LLMs) possess impressive reasoning capabilities, they struggle with time-sensitive question answering (QA) in long documents due to (1) the presence of (1) irrelevant noisy context, and (2) implicit expressions of temporal events. To address these challenges, we introduce Chain-of-Timeline (CoTime), a framework that constructs topic-relevant event timelines through structured code-style formalization. CoTime first extracts a high-level topic from the question (e.g., [subject]'s career history) to identify relevant temporal events in the document. These events are then organized into a temporal SQL-style schema, enabling CoTime to derive answers based on the question's specified time identifiers. Experimental results show that CoTime surpasses representative baselines.