Large language models (LLMs) such as GPT-4 and Gemini show strong reasoning ability but incur substantial computational cost in abductive reasoning settings. We present our system for "SemEval-2026 Task 12 — Abductive Event Reasoning: Towards Real-World Event Causal Inference for Large Language Models", which integrates knowledge graph (KG) evidence extraction with knowledge distillation to transfer structured reasoning from a large teacher model to a compact student model. Our approach ranks 8th in the shared task while achieving performance comparable to frontier LLMs at a fraction of the inference cost.