Triple

T3238993
Position Surface form Disambiguated ID Type / Status
Subject Coat of Many Colors E67922 entity
Predicate producer P490 FINISHED
Object Bob Ferguson E218573 NE FINISHED

How this triple was built (2 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: Bob Ferguson | Statement: [Coat of Many Colors, producer, Bob Ferguson]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bob Ferguson
Context triple: [Coat of Many Colors, producer, Bob Ferguson]
  • A. Bob Ferguson chosen
    Bob Ferguson was an American country music record producer known for his influential work with major artists in the 1960s and 1970s.
  • B. Ed Murray
    Ed Murray is an American politician who served as the 53rd mayor of Seattle and previously spent many years in the Washington State Legislature.
  • C. Mark Helfrich
    Mark Helfrich is an American football coach best known for leading the University of Oregon Ducks, including a run to the first College Football Playoff National Championship game in the 2014 season.
  • D. Mark Helfrich
    Mark Helfrich is an American film editor best known for his work on action and comedy films, including collaborations with director Brett Ratner.
  • E. Howard Cunningham
    Howard Cunningham is the affable, old-fashioned Milwaukee hardware store owner and father figure on the classic American sitcom "Happy Days."
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69ad858d27348190abb61c280b4c86a9 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69adaef4c0bc819095e4f84296fe7cb6 completed March 8, 2026, 5:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2774f93448190b8493b457636ae48 completed March 12, 2026, 8:20 a.m.
Created at: March 8, 2026, 3:08 p.m.