I've built an mcp for sparql and rdf. Used claude on iphone to turn pictures of archeological site information shields to transcription, to an ontology, to an rdf, to an er-model and sql statements, and then with mcp tool and claude desktop to save the data into parquet files on blobstorage and the ontology graph into a graph database. Then used it to query data from parquet (using duckdb), where sonnet 4 used the rdf graph to write better sql statements. Works quite well. Now in the process of using sonnet 4 to find the optimal system prompt for qwen coder to also handle rdf and sparql: i've given sonnet 4 access to qwen coder through an mcp tool, so it can trial and error different system prompt strategies. Results are promising, but can't compete with the quality of sonnet 4.

Graph database vendors are now trying to convince you that AI will be better with a graph database, but what i've seen so far indicates that the LLM just needs the RDF, not an actual database with data stored in triplets. Maybe because these were small tests, if you need to store a large amount of id mappings it may be different.