Humanities research depends on linking claims to precise evidence, such as juan, pages, passages, or time-stamped media segments. While large language models can assist with extraction and synthesis, their outputs remain difficult to use in scholarly work unless provenance, citation, and verification are explicitly controlled. This paper proposes an evidence-centric knowledge management system for humanities research. The system models sources as stable EvidenceUnits, extracts entities, relations, and events under schema constraints, and admits generated knowledge only after structural validation, evidence-pointer checking, and claim-level verification. Ambiguous or conflicting cases are routed to human review and retained in an audit trail. The main evaluation is a controlled document-modality study on an annotated subset of the Shiji. The system achieves micro-F1 scores of 0.84 for named entity recognition, 0.78 for relation extraction, and 0.82 for event trigger detection. Governance-layer analysis shows that evidence-pointer resolvability rises from 42.3% to 94.2%, while mis-citation and overreach fall to 4.1% and 5.3%. A minimal oral history audio smoke test further demonstrates that timestamped Audio EvidenceUnits can pass through the same governance workflow; though, it is not a full multimodal benchmark.
Bo An (Wed,) studied this question.