KG-assisted supervised fine-tuning depends on evidence selection, context compilation, and data loading. We present KG-SFT Contracts, a provenance-aware interface that audits these stages and links model comparisons to their data and scoring records. In a retrospective Russian enterprise case, a pool of 484 graph candidates contributed no graph-origin distractors to the compiled corpus, and exact loader replay discarded 40 of 946 intended examples before splitting. Removing annotated non-support context restored every row while preserving questions, targets, and verified support. Downstream comparisons remained statistically inconclusive. Controlled checks on a public graph-bearing fixture show how cross-record validation detects unresolved graph references and conflicting source revisions alongside role-aware repair. The installable implementation includes fixture-level CPU measurements, incremental adoption guidance, and an official RuBQ schema-import check. The case illustrates how lineage and retention audits make adaptation pipelines inspectable before model effects are attributed to their configured inputs.