Event extraction is a critical task that involves several interdependent sub-tasks. The complex interplay among these sub-tasks makes the overall task more challenging, particularly in low-resource scenarios where data availability is limited. However, the inherent logical coherence among these sub-tasks presents a promising avenue for addressing these challenges. This logical structure is particularly advantageous in low-resource settings, as it facilitates a deeper understanding of the tasks by the model and reduces dependence on available data. Building on this observation, we explore the logical structure of event extraction with a focus on low-resource scenarios. Specifically, we propose a three-step Chain-of-Thought pattern to guide the model through the logical reasoning process. Additionally, we design a step-wise navigator that dynamically provides the model with relevant knowledge. Empirical results demonstrate the robustness of our approach in low-resource event extraction.