Turning LLMs into interactive 3D historical maps using Wikidata QIDs and URL compression

Reddit r/ArtificialInteligence 08/16/26, 05:06 PM Tools

llm webgl 3d-visualization wikidata url-compression client-side historical-maps

Summary

A client-side WebGL application that uses LLMs to convert conversational outputs into interactive 3D historical maps, leveraging Wikidata for geospatial data and URL compression for state management.

I built this client-side WebGL application to demonstrate a zero-backend Prompt-to-Artifact pattern, turning unstructured conversational LLM outputs into spatial narrative visualizations. Data Source: Wikidata API for canonical entity coordinates, QIDs, and geospatial metadata, combined with LLM knowledge base retrieval. Tools Used: Three.js WebGL rendering engine, Custom System Prompt, Client-side URL parser. How the Data Pipeline Works: System Prompt Execution: A custom prompt forces models like ChatGPT, Claude, or Gemini to act as data compilers rather than text generators. Entity Resolution: The LLM resolves historical milestones to their respective Wikidata QIDs to obtain precise geospatial coordinates. URL State Compression: The entire sequence of events (factions, timelines, descriptions, locations) is serialized directly into a compressed URL payload. Client-Side Rendering: When opened, the WebGL frontend parses the URL hash in memory and reconstructs the animated 3D timeline without hitting a database or server backend. Try it out: https://mundu.app/create.html

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