The quantity and quality of vulnerability datasets are essential for developing deep learning solutions to vulnerability-related tasks. Due to the limited availability of vulnerabilities, a common approach to building such datasets is analyzing security patches in source code. However, existing security patches often suffer from inaccurate labels, insufficient contextual information, and undecidable patches that fail to clearly represent the root causes of vulnerabilities or their fixes. These issues introduce noise into the dataset, which can mislead detection models and undermine their effectiveness. To address these issues, we present mono, a novel LLM-powered framework that simulates human experts' reasoning process to construct reliable vulnerability datasets. mono introduces three key components to improve security patch datasets: (i) semantic-aware patch classification for precise vulnerability labeling, (ii) iterative contextual analysis for comprehensive code understanding, and (iii) systematic root cause analysis to identify and filter undecidable patches. Our comprehensive evaluation on the MegaVul benchmark demonstrates that mono can correct 31.0% of labeling errors, recover 89% of inter-procedural vulnerabilities, and reveals that 16.7% of CVEs contain undecidable patches. Furthermore, mono's enriched context representation improves existing models' vulnerability detection accuracy by 15%. We open source the framework mono and the dataset MonoLens in https://github.com/vul337/mono.