This is a fantastic walkthrough for anyone looking to bridge the gap between unstructured text and graph databases. A few thoughts:
The schema-first approach you demonstrate is crucial - I've seen knowledge graphs fall apart when entity types and relationships aren't well-defined upfront. Your Character/Ship ontology example makes this concrete.
For RAG applications specifically, there's an interesting trade-off between this extraction approach and vector embeddings. Knowledge graphs excel at relationship queries, while vectors handle semantic similarity. Combining both - using the graph for structured traversal and embeddings for fuzzy retrieval - is where things get powerful.
The MERGE vs CREATE distinction you included is a nice touch. In production pipelines, deduplication becomes critical when processing multiple documents that reference the same entities.
One thing I'd add: for larger-scale ingestion, you might want to batch Cypher statements. Each API call has overhead, and batching 100-1000 statements at once can significantly speed up bulk loads.
Thanks for the practical, end-to-end guide - the combination of Neo4j's visualization with a local LLM for extraction is a great pattern that more teams should adopt.