Triple

T6161668
Position Surface form Disambiguated ID Type / Status
Subject Dallas Buyers Club E137456 entity
Predicate starring P1507 FINISHED
Object Griffin Dunne E387968 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: Griffin Dunne | Statement: [Dallas Buyers Club, starring, Griffin Dunne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Griffin Dunne
Context triple: [Dallas Buyers Club, starring, Griffin Dunne]
  • A. Griffin Dunne chosen
    Griffin Dunne is an American actor, director, and producer known for roles in films like "An American Werewolf in London" and "After Hours."
  • B. Gil Pender
    Gil Pender is the nostalgic, time-traveling screenwriter protagonist of Woody Allen’s film "Midnight in Paris," portrayed by Owen Wilson.
  • C. William Devane
    William Devane is an American actor known for his intense, often authoritative roles in film and television, including prominent performances in projects like "Knots Landing" and "24."
  • D. Linden Ashby
    Linden Ashby is an American actor best known for his roles in films like Mortal Kombat and television series such as Melrose Place and Teen Wolf.
  • E. Joel Harlow
    Joel Harlow is an Academy Award-winning American makeup artist and special effects designer known for his work on films such as "Star Trek" and "Pirates of the Caribbean."
  • 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_69c008a54fc88190b6ce4416490ca79d completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c05d371484819090c18b62b095b49e completed March 22, 2026, 9:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69c14194d31081908e61a867f11117b4 completed March 23, 2026, 1:35 p.m.
Created at: March 22, 2026, 4:17 p.m.