Connecting S-101 Chart Data and the Feature Catalogue for LLM-Assisted Electronic Navigational Chart Production and Inspection

Interpreting S-101 Electronic Navigational Charts requires connecting chart objects and encoded attributes with Feature Catalogue (FC) definitions and Data Classification and Encoding Guide (DCEG) guidance. This study evaluates a read-only access layer that supplies this information to large language models. Five access conditions were compared using 65 public S-101 trial and test cells and two researcher-designed 90-question sets. Claude Opus 4.7, GPT-5.5, and Gemini 3.1 Pro each completed three runs under a uniform protocol, producing 8100 responses from predefined evidence inputs. Structured access to chart instances and FC/DCEG information achieved higher feature-identification and lexical catalogue-grounding scores than raw-record or generic retrieval across all three models. The principal access contrasts survived multiple-testing correction. Geometry-based re-scoring lowered nearby-feature scores while retaining the advantage over generic retrieval, highlighting the importance of spatial evidence selection. Adding structured source identifiers enabled source inspection, with no significant improvement in nearby-feature identification. These findings identify structured chart and catalogue access as a useful design basis for S-101 interpretation assistants. Operational benefits require evaluation with chart producers and validators; the proposed assistant complements formal validation.