Triple

T11991814
Position Surface form Disambiguated ID Type / Status
Subject Burn After Reading E285423 entity
Predicate hasCastMember P2308 FINISHED
Object Brad Pitt E42243 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: Brad Pitt | Statement: [Burn After Reading, hasCastMember, Brad Pitt]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Brad Pitt
Context triple: [Burn After Reading, hasCastMember, Brad Pitt]
  • A. Brad Pitt chosen
    Brad Pitt is an American actor and film producer renowned for his leading roles in major Hollywood films and for winning multiple Academy Awards.
  • B. Ian Affleck
    Ian Affleck is a Canadian theoretical physicist known for influential contributions to condensed matter physics and quantum field theory.
  • C. Sean Penn
    Sean Penn is an acclaimed American actor and filmmaker known for his intense, character-driven performances and multiple major awards, including two Academy Awards for Best Actor.
  • D. Russell Crowe
    Russell Crowe is an Academy Award–winning New Zealand–born actor renowned for intense, transformative performances in films such as Gladiator and A Beautiful Mind.
  • E. Edward Norton
    Edward Norton is an acclaimed American actor and filmmaker known for his intense, nuanced performances in films such as "Fight Club," "American History X," and "Birdman."
  • 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_69d6ab44a77c8190a652f4b27164e4ef completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d903b11ac481909866b611380792e7 completed April 10, 2026, 2:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69f4726696dc8190bf2a7aa43cb08b19 completed May 1, 2026, 9:29 a.m.
Created at: April 8, 2026, 9:46 p.m.