Retaining Splink Match Scores as Graph Edge Properties Improves Graph RAG Accuracy

Most entity resolution pipelines discard match probabilities after applying a binary match/non-match threshold, losing valuable confidence information before it reaches downstream systems. A developer building er-api, a multilingual entity resolution service for Korean, Japanese, Chinese, and English corporate data, chose instead to preserve Splink match probabilities as edge properties on SAME_AS relationships in the knowledge graph. This allows different retrieval queries to apply different confidence thresholds — for example, legal compliance queries can require 95% confidence while exploratory searches may accept 70%. During graph traversal for Graph RAG, the system propagates the worst-case confidence score along each path, so language model prompts can explicitly flag low-confidence identity claims. The approach also enables automatic approval of high-confidence matches while routing uncertain ones to a human review queue.