Triple

T14083782
Position Surface form Disambiguated ID Type / Status
Subject Babylon (2022 film) E338936 entity
Predicate writer P1360 FINISHED
Object Damien Chazelle E221277 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: Damien Chazelle | Statement: [Babylon (2022 film), writer, Damien Chazelle]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Damien Chazelle
Context triple: [Babylon (2022 film), writer, Damien Chazelle]
  • A. Damien Chazelle chosen
    Damien Chazelle is an American filmmaker and screenwriter known for his stylish, music-driven dramas such as "Whiplash" and the Oscar-winning "La La Land."
  • B. Derek Cianfrance
    Derek Cianfrance is an American filmmaker known for his emotionally intense, character-driven dramas such as "Blue Valentine" and "The Place Beyond the Pines."
  • C. Alex Gansa
    Alex Gansa is an American television writer and producer best known for co-creating and showrunning the acclaimed political thriller series "Homeland."
  • D. Daniel Kwan
    Daniel Kwan is an American filmmaker best known as one half of the directing duo Daniels, who co-wrote and co-directed the acclaimed film "Everything Everywhere All at Once."
  • E. Craig Gillespie
    Craig Gillespie is an Australian-American film director known for character-driven comedies and dramas such as "Lars and the Real Girl" and the biographical film "I, Tonya."
  • 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_69d81c687b0c819087fd9ed4198403f8 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de5ede40048190b465e909565730c1 completed April 14, 2026, 3:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcd0a3e55c81909b52f618e9076dd2 completed May 7, 2026, 5:49 p.m.
Created at: April 9, 2026, 10:21 p.m.