Knowledge graph of restaurants and chefs, built using LLMs
Hi HN! My latest side project is knowledge graph that maps the French culinary network using data extracted from restaurant reviews from LeFooding.com. The project uses LLMs to extract structured information from unstructured text. Some technical aspects you may be interested in: - Used structured generation to reliably parse unstructured text into a consistent schema - Tested multiple models (Mistral-7B-v0.3, Llama3.2-3B, gpt4o-mini) for information extraction - Created an interactive visualization using gephi-lite and Retina (WebGL) - Built (with Claude) a simple Flask web app to clean and…
Knowledge graph of restaurants and chefs, built using LLMs launched on March 3, 2025 with 201 votes, #189 of 7,215 launches that month and more than 95% of that year's launches. The site was still online in September 2026, 1 year on.
In plain words
This project creates an interactive knowledge graph mapping the French culinary network by extracting structured data from restaurant reviews using large language models. It processes unstructured text from LeFooding.com into a consistent schema, tested across multiple models including open-source and commercial options. The result is visualized as an interactive network using WebGL technology, accessible through a web interface built with Flask. The project demonstrates cost-effective information extraction, processing 2,000 reviews for under one euro.
written from the facts on this page · September 2026
From the sources
Creating a knowledge graph of restaurants featured in lefooding.com reviews using `outlines` and `gpt4o-mini`.
This post was shared by .txt on Linkedin and Bluesky , and discussed on Hacker News . For French restaurant intel, lefooding.com is the definitive data source. Their anonymous critics conduct systematic reviews of establishments across France (and now Belgium), documenting their findings in a witty - if peculiar - style. Beyond choosing the place for a delicious night out, they can be used to map and understand France’s culinary network. A network is composed of nodes and edges. Nodes represent entities, such as people or restaurants. Edges represent the relationships between people and restaurants, or absence thereof. For the French restaurant scene, nodes represent both people and…from theophilecantelob.re
In the maker’s words, at launch
Hi HN! My latest side project is knowledge graph that maps the French culinary network using data extracted from restaurant reviews from LeFooding.com. The project uses LLMs to extract structured information from unstructured text. Some technical aspects you may be interested in: - Used structured generation to…
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