Triple

T16910084
Position Surface form Disambiguated ID Type / Status
Subject Death to Smoochy E410170 entity
Predicate productionCompany P490 FINISHED
Object FilmFour E114293 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: FilmFour | Statement: [Death to Smoochy, productionCompany, FilmFour]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: FilmFour
Context triple: [Death to Smoochy, productionCompany, FilmFour]
  • A. FilmFour chosen
    FilmFour is a British film production company and former television channel associated with Channel 4, known for backing distinctive independent and arthouse films.
  • B. FilmScene cinema
    FilmScene cinema is an independent, nonprofit movie theater and film arts organization known for showcasing arthouse, foreign, and documentary films in Iowa City, Iowa.
  • C. VideoFilmes
    VideoFilmes is a Brazilian film production company known for its work on acclaimed art-house and independent films.
  • D. Vintry
    Vintry is one of the historic wards of the City of London, traditionally associated with the wine trade along the River Thames.
  • E. Flims
    Flims is a Swiss alpine resort village in the canton of Graubünden, known for its skiing, hiking, and scenic mountain landscapes.
  • 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_69d886c7b1e481908c3766dfa8c13458 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3ca3ca0c481909ff361ccf4a922e3 completed April 18, 2026, 6:15 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00c7bb4ac481909318d3d61a2d10e1 completed May 10, 2026, 6 p.m.
Created at: April 10, 2026, 5:30 a.m.