Triple

T3205039
Position Surface form Disambiguated ID Type / Status
Subject Why Did I Get Married? E67140 entity
Predicate stars P1956 FINISHED
Object Richard T. Jones
Richard T. Jones is an American actor known for his roles in film and television, including prominent performances in dramas and thrillers.
 E494883 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: Richard T. Jones | Statement: [Why Did I Get Married?, stars, Richard T. Jones]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Richard T. Jones
Context triple: [Why Did I Get Married?, stars, Richard T. Jones]
  • A. Todd R. Jones
    Todd R. Jones is a writer known for his work on the animated film "Rio."
  • B. Joel J. Richard
    Joel J. Richard is a film composer best known for co-scoring high-octane action movies, including entries in the John Wick franchise.
  • C. F. Richard Jones
    F. Richard Jones was an American film director and producer of the silent and early sound era, known for his work on comedies and collaborations with major studios like Mack Sennett and Hal Roach.
  • D. Robert C. Jones
    Robert C. Jones was an American film editor and screenwriter known for his work on numerous notable films and for winning an Academy Award for Best Film Editing.
  • E. Hugh G. Jones
    Hugh G. Jones was an architect known for his role in designing Toronto’s historic Union Station.
  • 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: Richard T. Jones
Triple: [Why Did I Get Married?, stars, Richard T. Jones]
Generated description
Richard T. Jones is an American actor known for his roles in film and television, including prominent performances in dramas and thrillers.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Richard T. Jones
Target entity description: Richard T. Jones is an American actor known for his roles in film and television, including prominent performances in dramas and thrillers.
  • A. Todd R. Jones
    Todd R. Jones is a writer known for his work on the animated film "Rio."
  • B. Joel J. Richard
    Joel J. Richard is a film composer best known for co-scoring high-octane action movies, including entries in the John Wick franchise.
  • C. F. Richard Jones
    F. Richard Jones was an American film director and producer of the silent and early sound era, known for his work on comedies and collaborations with major studios like Mack Sennett and Hal Roach.
  • D. Robert C. Jones
    Robert C. Jones was an American film editor and screenwriter known for his work on numerous notable films and for winning an Academy Award for Best Film Editing.
  • E. Hugh G. Jones
    Hugh G. Jones was an architect known for his role in designing Toronto’s historic Union Station.
  • 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_69ad8589bd988190afa7ed2bdffb7b33 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69adaa559848819082d1e61f586278dd completed March 8, 2026, 4:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69beba392ad08190a66ccf58edecb722 completed March 21, 2026, 3:33 p.m.
NEDg Description generation batch_69bebe2ca1cc8190bb7b5fb5b8fed9c7 completed March 21, 2026, 3:50 p.m.
NED2 Entity disambiguation (via description) batch_69bebe8284d08190be8e991246662481 completed March 21, 2026, 3:51 p.m.
Created at: March 8, 2026, 3:07 p.m.