Software engineering research increasingly relies on large, richly annotated corpora of real-world code. Still, researchers typically assemble these with ad-hoc scripts or static, pre-built datasets that offer no control over corpus composition. This leaves a gap for a reusable tool that goes below the repository level and builds annotated, function- and class-level code corpora under researcher-specified criteria. We present SIEVE (Software Ingestion & Extraction for Verifiable Evaluation), an open-source, parameterized GitHub corpus builder that, unlike repository-level mining tools, works at code granularity: it locates individual functions, methods, and classes in repositories and extracts them as records, giving direct control over corpus construction based on several criteria, including widely used programming languages (Python, Java, JavaScript, C++), repository quality, and creation date. To support software engineering research and LLM evaluation on real-world code, SIEVE extracts CodeSearchNet- and OpenClassGen-compatible records, each annotated with 22 structural metrics and AST-derived features, and optionally with a CodeBERT-based P(AI-generated) score (F1=0.9478, AUROC=0.9902), alongside the third-party dependencies declared in each repository's package manifest. These annotations enable researchers to conduct structural analyses, investigate code dependencies, assess AI-generated code, and construct customized benchmarks for evaluating LLMs, including under temporal constraints. A manual validation of 400 records in four languages and two granularities shows 100% accuracy in code metrics and import detection, 99.0% in source completeness, and 99.5% in metadata accuracy. SIEVE is publicly available with a live demo at https://mrahman2025-sieve.hf.space.

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