Triple

T1396238
Position Surface form Disambiguated ID Type / Status
Subject Maynard Jackson E30670 entity
Predicate succeededBy P78 FINISHED
Object Bill Campbell
Bill Campbell is an American politician who served as mayor of Atlanta, Georgia, during the 1990s.
E161958 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Bill Campbell | Statement: [Maynard Jackson, succeededBy, Bill Campbell]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bill Campbell
Context triple: [Maynard Jackson, succeededBy, Bill Campbell]
  • A. Ted Cheesman
    Ted Cheesman was a film editor best known for his work on classic Hollywood productions, including the 1933 monster film "King Kong."
  • B. Tim McClelland
    Tim McClelland is a former Major League Baseball umpire known for his long tenure, distinctive strike zone, and involvement in several high-profile postseason games.
  • C. Rob McKenna
    Rob McKenna is a perpetually rain-plagued lorry driver in Douglas Adams' "So Long, and Thanks for All the Fish," humorously revealed to be a Rain God unknowingly worshipped by clouds.
  • D. Rob McKenna
    Rob McKenna is an American attorney and politician best known for serving as the Attorney General of Washington State.
  • E. Bill Marshall
    Bill Marshall was a Canadian film producer and cultural entrepreneur best known for co-founding and helping establish the Toronto International Film Festival as a major global cinema event.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Bill Campbell
Triple: [Maynard Jackson, succeededBy, Bill Campbell]
Generated description
Bill Campbell is an American politician who served as mayor of Atlanta, Georgia, during the 1990s.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Bill Campbell
Target entity description: Bill Campbell is an American politician who served as mayor of Atlanta, Georgia, during the 1990s.
  • A. Ted Cheesman
    Ted Cheesman was a film editor best known for his work on classic Hollywood productions, including the 1933 monster film "King Kong."
  • B. Tim McClelland
    Tim McClelland is a former Major League Baseball umpire known for his long tenure, distinctive strike zone, and involvement in several high-profile postseason games.
  • C. Rob McKenna
    Rob McKenna is a perpetually rain-plagued lorry driver in Douglas Adams' "So Long, and Thanks for All the Fish," humorously revealed to be a Rain God unknowingly worshipped by clouds.
  • D. Rob McKenna
    Rob McKenna is an American attorney and politician best known for serving as the Attorney General of Washington State.
  • E. Bill Marshall
    Bill Marshall was a Canadian film producer and cultural entrepreneur best known for co-founding and helping establish the Toronto International Film Festival as a major global cinema event.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69a498fd4e408190bd73eca30ea9754c completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c37fd6e0819084d610ef041db3af completed March 1, 2026, 10:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69ace56e32608190a03485d7cb5941a8 completed March 8, 2026, 2:56 a.m.
NEDg Description generation batch_69ace6527e088190a1ea0ec178b4e0ce completed March 8, 2026, 3 a.m.
NED2 Entity disambiguation (via description) batch_69ace6b543ec819080a5ddeed0273644 completed March 8, 2026, 3:02 a.m.
Created at: March 1, 2026, 7:59 p.m.