Ask which subsidiaries a conglomerate owns, and a language model returns a plausible list, unverifiable and partly wrong; in compliance or supervision, that will not do. So instead of trusting the answer, a local open-weight model translates each question into an auditable SPARQL query over the GLEIF register, returning the query and its source records. A 20B model answers 86.7% of held-out questions correctly, always with a valid query and declining the unanswerable, and no larger open or closed model does better. The method fits any ontology-described source; code and benchmark are released.

The AKBC 2026 shared task asks a language model to recover complete object sets closed-book. SetProbe anchors each subject, samples several candidate sets, and aggregates them per relation, reaching 0.513 macro F1. Sample count, aggregation rule, and abstention thresholds each move the score by less than the evaluation set’s own 0.045 confidence interval; only the choice of model does. The binding constraint is elementary: closed-book prediction cannot recover an object that appears in no sample.