Triple

T14874129
Position Surface form Disambiguated ID Type / Status
Subject Phoebe in Wonderland E349822 entity
Predicate distributor P1951 FINISHED
Object ThinkFilm E883676 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: ThinkFilm | Statement: [Phoebe in Wonderland, distributor, ThinkFilm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ThinkFilm
Context triple: [Phoebe in Wonderland, distributor, ThinkFilm]
  • A. ThinkFilm chosen
    ThinkFilm was an independent film distribution company known for releasing arthouse and specialty films in the early 2000s.
  • B. Alliance Films
    Alliance Films was a major Canadian film distribution and production company known for releasing a wide range of independent and international movies in Canada and other markets.
  • C. Tartan Films
    Tartan Films was a UK-based independent film distribution company known for releasing cult, arthouse, and international cinema, particularly Asian extreme films.
  • D. Icon Films
    Icon Films is a British television production company known for creating popular factual and wildlife documentary series, including the hit show "River Monsters."
  • E. Rook Films
    Rook Films is a British independent film production company known for its distinctive, often surreal and genre-bending movies.
  • 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_69d822ee4f408190b6ac3b2fa434f0df completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69ded5e3e5d48190a132f2cf012b01e2 completed April 15, 2026, 12:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe6b52c12481908d0173a2a3ed854b completed May 8, 2026, 11:01 p.m.
Created at: April 10, 2026, 1:55 a.m.