Triple

T10514697
Position Surface form Disambiguated ID Type / Status
Subject The Hurt Locker E248001 entity
Predicate productionCompany P490 FINISHED
Object Voltage Pictures E570587 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: Voltage Pictures | Statement: [The Hurt Locker, productionCompany, Voltage Pictures]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Voltage Pictures
Context triple: [The Hurt Locker, productionCompany, Voltage Pictures]
  • A. Voltage Pictures chosen
    Voltage Pictures is an American independent film production and financing company known for backing a wide range of commercially successful and critically acclaimed movies.
  • B. Phoenix Pictures
    Phoenix Pictures is an American film production company known for producing critically acclaimed movies such as "Black Swan" and "The Thin Red Line."
  • C. Adobe Stock
    Adobe Stock is Adobe’s royalty-free stock content service offering millions of photos, illustrations, vectors, videos, and other creative assets for use in design and media projects.
  • D. Lewis Pictures
    Lewis Pictures is a South Korean film production company known for backing acclaimed works such as Bong Joon-ho’s fantasy drama "Okja."
  • E. Nu Image
    Nu Image is a film production company known for producing a wide range of genre movies, including action, thriller, and crime dramas.
  • 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_69d381c4aa948190942e1d803143fb0e completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d509cade0c81908fcbd54a90106bf9 completed April 7, 2026, 1:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69d90df141488190a2674546aa437de8 completed April 10, 2026, 2:49 p.m.
Created at: April 6, 2026, 12:27 p.m.