Triple

T7773117
Position Surface form Disambiguated ID Type / Status
Subject LaVona Golden E179120 entity
Predicate productionCompanyOfWork P25234 FINISHED
Object AI-Film E452296 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: AI-Film | Statement: [LaVona Golden, productionCompanyOfWork, AI-Film]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: AI-Film
Context triple: [LaVona Golden, productionCompanyOfWork, AI-Film]
  • A. AI-Film chosen
    AI-Film is a film production company known for its involvement in notable independent and biographical movies such as "I, Tonya."
  • B. FilmEngine
    FilmEngine is an American film production company known for developing and producing feature films such as the crime thriller "Lucky Number Slevin."
  • C. Making Movies
    Making Movies is a widely respected memoir and craft-focused book in which acclaimed film director Sidney Lumet explains his practical approach to filmmaking.
  • D. Agfa film production
    Agfa film production was a major photographic film manufacturing operation historically associated with the German company Agfa, known for producing widely used camera and motion picture films.
  • E. Adobe Firefly (generative AI services)
    Adobe Firefly is Adobe’s suite of generative AI tools designed to create and enhance images, text effects, and other creative assets within its digital media ecosystem.
  • 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_69c69f30602c819082ab52cd4af5c592 completed March 27, 2026, 3:16 p.m.
NER Named-entity recognition batch_69c7046048688190a6cbc64e82b58eca completed March 27, 2026, 10:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8c7ee407881908e591d216c504b24 completed March 29, 2026, 6:34 a.m.
Created at: March 27, 2026, 4:11 p.m.