Triple

T20118989
Position Surface form Disambiguated ID Type / Status
Subject How High E490546 entity
Predicate castMember P1668 FINISHED
Object Jeffrey Jones NE NERFINISHED

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: Jeffrey Jones | Statement: [How High, castMember, Jeffrey Jones]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jeffrey Jones
Context triple: [How High, castMember, Jeffrey Jones]
  • A. Jeffrey Jones chosen
    Jeffrey Jones is an American character actor best known for his roles in films such as "Ferris Bueller's Day Off," "Beetlejuice," and "Amadeus."
  • B. William Gooding
    William Gooding was a 19th-century American engineer best known for designing and overseeing the construction of major canal infrastructure in the Midwest.
  • C. Joseph Marcell
    Joseph Marcell is a British actor best known for playing the witty butler Geoffrey Butler on the sitcom "The Fresh Prince of Bel-Air."
  • D. Michael Pennington
    Michael Pennington is a distinguished English actor and director, particularly renowned for his work in classical theatre and Shakespearean performance.
  • E. Eric Warner
    Eric Warner is a relatively obscure individual whose primary distinguishing feature is sharing the common surname Warner, with no widely recognized public achievements or roles documented.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69da62636cc08190982cc71733a17b8d completed April 11, 2026, 3:01 p.m.
NER Named-entity recognition batch_69e6673c32bc8190a52875961fbcc5e2 completed April 20, 2026, 5:49 p.m.
Created at: April 11, 2026, 11:30 p.m.