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Zero-config entity resolution. The zero-tuning Fellegi-Sunter path beats hand-rolled Splink head-to-head; scales from a CSV to a verified 100M-row dedupe in 9.2 min on Ray. Fuzzy/exact/probabilistic + PPRL + LLM, identity graph. Python + edge-safe TypeScript (optional WASM), SQL-native in Postgres & DuckDB, MCP/REST + dbt/Airflow.

110 starsPython

GoldenMatch – Entity resolution with LLM scoring, 97% F1, no Spark

by benzsevern·Mar 21, 2026·3 points·0 comments
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AI Analysis

●●SolidBig BrainSolve My Problem

Fellegi-Sunter matching with active learning beats Dedupe.io on complex datasets.

Strengths
  • •Fellegi-Sunter EM-trained probabilities with automatic threshold estimation built in.
  • •Active learning TUI: label 10 borderline pairs, instantly retrain classifier.
  • •Privacy-preserving bloom filter transforms for fuzzy matching on encrypted PII.
Weaknesses
  • •Entity resolution is crowded: Dedupe.io, OpenRefine, and commercial tools already exist.
  • •LLM scoring and Vertex AI embeddings require paid API keys for best accuracy.
Category
Data
Target Audience

Data engineers, analysts working with messy duplicate records

Similar To

Dedupe.io · OpenRefine · Tamr

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