Flowcharts are widely used in business management and software development to represent process logic and user interactions. Understanding the semantics they encode is essential for downstream applications such as visual question answering. Existing methods either detect components and reconstruct topology, which suffers from cascaded errors and rigid shape assumptions, or feed the whole image to a vision language model (VLM), which dilutes attention on dense diagrams and often reverses edge directions. Inspired by cognitive science findings that humans read flowcharts by tracing arrows, we propose TRACE (Triplet Recovery via Arrowhead-Centric Extraction), a three-stage framework. An offline stage synthesizes aligned training data from text-format flowchart sources (e.g., Mermaid, BPMN XML) and fine-tunes the two models used online: a detector that locates every arrowhead, and then a VLM that extracts one triplet per arrowhead. Each output triplet is grounded to a detected arrowhead, enabling traceable diagnosis. Experiments show that TRACE substantially improves flowchart understanding accuracy and improves downstream QA accuracy.