Triple

T20996948
Position Surface form Disambiguated ID Type / Status
Subject Fanny and Alexander E517173 entity
Predicate distributor P1951 FINISHED
Object Svensk Filmindustri 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: Svensk Filmindustri | Statement: [Fanny and Alexander, distributor, Svensk Filmindustri]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Svensk Filmindustri
Context triple: [Fanny and Alexander, distributor, Svensk Filmindustri]
  • A. Svensk Filmindustri chosen
    Svensk Filmindustri is a major Swedish film production and distribution company, historically one of the country’s most influential studios.
  • B. Swedish Film Institute
    The Swedish Film Institute is a national organization that supports, funds, and promotes Swedish cinema both domestically and internationally.
  • C. Cinematograph AB
    Cinematograph AB is a Swedish film production company best known for producing Ingmar Bergman’s films, including the acclaimed drama "Cries and Whispers."
  • D. Nordisk Film
    Nordisk Film is a major Danish entertainment company and one of the world’s oldest film studios, known for producing and distributing films across the Nordic region.
  • E. Filmstaden AB
    Filmstaden AB is Sweden’s largest cinema chain, operating multiplex movie theaters across the country.
  • 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_69e0b5006e2881909fc2383f841740cc completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e6fc21838081909872eed21bc12a08 completed April 21, 2026, 4:25 a.m.
Created at: April 16, 2026, 1:51 p.m.