Show HN: What 180k words look like as a temporal knowledge graph (Oz series)
A researcher has built a temporal knowledge graph of the Oz book series using LLMs and NLP to track character relationships and events across 180,000 words. The project provides a searchable, verifiable model of the story's narrative structure.
Why it matters
It showcases advanced applications of LLMs in literary analysis and structured data extraction from unstructured text.
The graph is free to explore and requires no registration. SynapTale builds a model of a story as a temporal graph made up of nodes (entities) and edges (their actions and relationships). The graph is not a visualization of the wiki. The wiki, timelines, relationship histories, and analytics are projections of the graph. The current demo contains 232 entities, 1,852 edges, and a snapshot of the story’s state at every chapter. By chapter 100, it still remembers a promise made in chapter 8 and turns the story into a set of source-verifiable facts. The most interesting things can be found in the graph itself and in the Analytics tab. A few things I found: 1. The character with the highest kill count is the Tin Woodman—the same character who cries over a beetle he accidentally crushed. Dorothy comes second, with three killing events. 2.
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