WHEN IS GRAPH EVIDENCE ENOUGH FOR FINE- GRAINED DISCRIMINATION IN INDUCTIVE KGC?

Knowledge graph foundation models (KGFMs) can reject implausible entities yet fail to distinguish the correct answer from relation-compatible alternatives. For both ULTRA and FLOCK, replacing random candidate pools with relation-compatible pools of matched pre-filter size reduces mean reciprocal rank by 0.238–0.261, exposing a fine-grained discrimination failure. We investigate candidate-conditioned evidence retrieval as a remedy: a KGFM proposes candidates, and an LLM verifies each hypothesized relation against externally acquired support. Holding the shortlists and verifier model fixed, we compare entity names, bounded graph context, and Web evidence, and observe two contrasting evidence regimes. On FB15k-237Ind v1, Web verification improves conditional mean reciprocal rank over graph verification by 0.077–0.154 and also outperforms the original KGFM ranking. On pooled NELL-995Ind v4 samples, graph context improves over names, whereas Web evidence yields no statistically reliable further gain. Retrieval analysis associates failures with missing answer support or competing evidence: evidence availability alone does not ensure discrimination. These findings demonstrate the conditional value of candidate-level evidence acquisition as a complement to structural ranking and motivate selective retrieval for resolving plausible alternatives under the evaluated protocols.