Triple

T3147773
Position Surface form Disambiguated ID Type / Status
Subject Peacock E65803 entity
Predicate contentSource P409 FINISHED
Object Focus Features E48067 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: Focus Features | Statement: [Peacock, contentSource, Focus Features]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Focus Features
Context triple: [Peacock, contentSource, Focus Features]
  • A. Focus Features chosen
    Focus Features is an American film production and distribution company known for releasing critically acclaimed independent and art-house movies.
  • B. Marché du Film
    Marché du Film is the Cannes Film Festival’s major international film market, where industry professionals buy, sell, and promote films and projects.
  • C. Direct Cinema
    Direct Cinema is a documentary filmmaking movement characterized by unobtrusive, observational techniques that aim to capture reality as it unfolds without scripted narration or interference.
  • D. New Yorker Films
    New Yorker Films is an American film distribution company known for bringing international, independent, and art-house cinema to U.S. audiences.
  • E. Cinéfondation
    Cinéfondation is a Cannes Film Festival program dedicated to discovering and promoting emerging filmmakers, primarily through showcasing short and medium-length films from film schools around the world.
  • 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_69ad8584485081909ed529e890cadc4a completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada59a54188190a2e020fd4004d734 completed March 8, 2026, 4:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69b224f53bbc81908416272cd48af69e completed March 12, 2026, 2:29 a.m.
Created at: March 8, 2026, 3:05 p.m.