Developer fixes entity resolution pipeline after news text exposed critical flaws

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A developer building the entity resolution pipeline at 2asy.ai discovered major gaps after expanding from structured registry data to news articles, triggering two weeks of cleanup. The core problem was that news sources use informal company names — such as '삼성' instead of '삼성전자 주식회사' — which caused the pipeline's character-based blocking rule to match a short mention against every Samsung subsidiary at once. To address this, the developer added a post-scoring filter that uses surrounding article context, such as nearby entity mentions and sector keywords, to disambiguate top-scoring candidates. A stricter confidence threshold was also introduced specifically for short or informal name mentions, requiring a 0.90 score before merging them to a registry entry. However, the developer acknowledges the underlying benchmark still uses only registry-vs-registry pairs, leaving precision and recall for news-domain mentions unmeasured.

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