{"schema_version":"onlylabs.public_signal.v1","title":"OpenAI Writing: Discovering types for entity disambiguation","description":"OpenAI writing signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/ee3bc142-3859-48f4-84a4-e8281d1c404b","json_url":"https://onlylabs.fyi/signals/ee3bc142-3859-48f4-84a4-e8281d1c404b/signal.json","generated_at":"2026-06-08T15:47:06.741+00:00","org":{"slug":"openai","name":"OpenAI","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/openai","dossier_json_url":"https://onlylabs.fyi/labs/openai/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/ee3bc142-3859-48f4-84a4-e8281d1c404b","signal_json":"https://onlylabs.fyi/signals/ee3bc142-3859-48f4-84a4-e8281d1c404b/signal.json","source":"https://openai.com/index/discovering-types-for-entity-disambiguation","lab_dossier":"https://onlylabs.fyi/labs/openai","lab_dossier_json":"https://onlylabs.fyi/labs/openai/dossier.json","analysis":"https://onlylabs.fyi/analysis/openai","analysis_json":"https://onlylabs.fyi/analysis/openai/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/openai/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":"https://onlylabs.fyi/topics/talking","topic_signals_json":"https://onlylabs.fyi/topics/talking/signals.json","topic_feed":"https://onlylabs.fyi/topics/talking/feed.xml","data_business":null},"answer_pack":{"answer":"OpenAI published Discovering types for entity disambiguation. 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attention.."},{"subject":"Discovering types for entity disambiguation","predicate":"has source host","object":"openai.com","text":"Discovering types for entity disambiguation has source host openai.com."},{"subject":"Discovering types for entity disambiguation","predicate":"has lab","object":"OpenAI","text":"Discovering types for entity disambiguation has lab OpenAI."},{"subject":"Discovering types for entity disambiguation","predicate":"has signal desk","object":"talking","text":"Discovering types for entity disambiguation has signal desk talking."},{"subject":"Discovering types for entity disambiguation","predicate":"has source host","object":"openai.com","text":"Discovering types for entity disambiguation has source host openai.com."},{"subject":"Discovering types for entity disambiguation","predicate":"has watch term","object":"Data pipeline","text":"Discovering types for entity disambiguation has watch term Data pipeline."},{"subject":"Discovering types for entity disambiguation","predicate":"has watch term","object":"Infrastructure","text":"Discovering types for entity disambiguation has watch term Infrastructure."}]},"intelligence":{"signal_desk":"talking","answer":"OpenAI published Discovering types for entity disambiguation. This talking signal gives public context for research themes, product direction, policy, or launch framing. 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For example, given a sentence like “the prey saw the jaguar cross the jungle”, rather than trying to reason directly whether jaguar means the car, the animal, or something else, the system plays “20 questions” with a pre-chosen set of categories. This approach gives a big boost in state-of-the-art on several entity disambiguation datasets. Loading... We achieve 94.88% accuracy on CoNLL (YAGO)⁠(previous state of the arts: 91.50⁠%, 91.70⁠%) and 90.85% on TAC KBP 2010 challenge⁠(previous state of the arts: 87.20⁠%, and 87.70⁠%). Previous methods used distributed representations⁠. Types can go almost all the way on these tasks, as perfect type prediction would give accuracies of 98.6-99%. High-level overview Our system uses the following steps: 1. Extract every Wikipedia-internal link to determine, for each word, the..."},"evidence_pages":[{"url":"https://openai.com/index/discovering-types-for-entity-disambiguation","final_url":"https://openai.com/index/discovering-types-for-entity-disambiguation","title":"Discovering types for entity disambiguation","http_status":200,"content_type":null,"capture_method":"exa","fetched_at":"2026-06-08T15:47:06.741+00:00","bytes":null,"raw_path":null,"content_hash":null,"excerpt_chars":1200,"truncated":true,"excerpt":"Discovering types for entity disambiguation | OpenAI February 7, 2018 Publication Discovering types for entity disambiguation Read paper Loading… Share We’ve built a system for automatically figuring out which object is meant by a word by having a neural network decide if the word belongs to each of about 100 automatically-discovered “types” (non-exclusive categories). For example, given a sentence like “the prey saw the jaguar cross the jungle”, rather than trying to reason directly whether jaguar means the car, the animal, or something else, the system plays “20 questions” with a pre-chosen set of categories. This approach gives a big boost in state-of-the-art on several entity disambiguation datasets. Loading... We achieve 94.88% accuracy on CoNLL (YAGO)⁠(previous state of the arts: 91.50⁠%, 91.70⁠%) and 90.85% on TAC KBP 2010 challenge⁠(previous state of the arts: 87.20⁠%, and 87.70⁠%). Previous methods used distributed representations⁠. Types can go almost all the way on these tasks, as perfect type prediction would give accuracies of 98.6-99%. High-level overview Our system uses the following steps: 1. 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