This study evaluates how Large Language Models (LLMs) perform deep legal reasoning on Taiwanese Status Law questions and investigates how Chain-of-Thought (CoT) prompting affects interpretability, alignment, and generalization. Using a two-stage evaluation framework, we first decomposed six real legal essay questions into 68 sub-questions covering issue spotting, statutory application, and inheritance computation. In Stage Two, full-length answers were collected under baseline and CoT-prompted conditions. Four LLMs—ChatGPT-4o, Gemini, Grok3, and Copilot—were tested. Results show CoT prompting significantly improved accuracy for Gemini (from 83.2% to 94.5%, p < 0.05) and Grok3, with moderate but consistent gains for ChatGPT and Copilot. Human evaluation of full-length responses revealed CoT answers received notably higher scores in issue coverage and reasoning clarity, with ChatGPT and Gemini gaining +2.67 and +1.92 points respectively. Despite these gains, legal misclassifications persist, highlighting alignment gaps between surface-level fluency and expert legal reasoning. This work opens the black box of legal NLU by tracing LLM reasoning chains, quantifying performance shifts under structured prompting, and providing a diagnostic benchmark for complex, open-ended legal tasks beyond multiple-choice settings.