Triple

T23388950
Position Surface form Disambiguated ID Type / Status
Subject Trash Fire E593957 entity
Predicate productionCompany P490 FINISHED
Object Snowfort Pictures NE NERFINISHED

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: Snowfort Pictures | Statement: [Trash Fire, productionCompany, Snowfort Pictures]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Snowfort Pictures
Context triple: [Trash Fire, productionCompany, Snowfort Pictures]
  • A. Snowfort Pictures chosen
    Snowfort Pictures is an independent film production company known for producing genre-driven and cult-favorite horror, thriller, and sci-fi movies.
  • B. 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."
  • C. Nomadic Pictures
    Nomadic Pictures is a Canadian film and television production company known for producing series such as Hell on Wheels and other genre and drama projects.
  • D. Indelible Pictures
    Indelible Pictures is a film production company known for producing the skateboarding drama "Lords of Dogtown."
  • E. Lopert Pictures
    Lopert Pictures was an American film distribution company known for releasing foreign and art-house films to U.S. audiences, often in collaboration with major studios.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e25d2754fc819085deea939bde60ab completed April 17, 2026, 4:17 p.m.
NER Named-entity recognition batch_69f1a499bad88190afca1afb2e3fddb0 completed April 29, 2026, 6:26 a.m.
Created at: April 17, 2026, 5:35 p.m.