Triple

T15702810
Position Surface form Disambiguated ID Type / Status
Subject The Bling Ring E380633 entity
Predicate productionCompany P490 FINISHED
Object Nala Films E678360 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: Nala Films | Statement: [The Bling Ring, productionCompany, Nala Films]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nala Films
Context triple: [The Bling Ring, productionCompany, Nala Films]
  • A. Nala Films chosen
    Nala Films is an independent film production company known for financing and producing critically acclaimed feature films.
  • B. Cinelou Films
    Cinelou Films is an independent American film production company known for producing character-driven dramas such as the 2014 film "Cake."
  • C. Rhea Films
    Rhea Films is a film production company known for collaborating on independent, critically acclaimed movies such as the crime thriller "Good Time."
  • D. Nemo Films
    Nemo Films is a film production company known for producing the science fiction drama series "Silo."
  • E. Nyerai Films
    Nyerai Films is a Zimbabwean film production company known for creating socially conscious, women-centered stories under the leadership of writer and filmmaker Tsitsi Dangarembga.
  • 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_69d86d99e860819094b6957cde470f2c completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e04f6e965881909319f85c51c6fb74 completed April 16, 2026, 2:54 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff7577a3348190912fad48f7d8599e completed May 9, 2026, 5:57 p.m.
Created at: April 10, 2026, 4:45 a.m.