Triple

T15492458
Position Surface form Disambiguated ID Type / Status
Subject Mauritz Stiller E378726 entity
Predicate employer P7 FINISHED
Object Svensk Filmindustri E686672 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: Svensk Filmindustri | Statement: [Mauritz Stiller, employer, Svensk Filmindustri]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Svensk Filmindustri
Context triple: [Mauritz Stiller, employer, 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. 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.
  • D. Film i Väst AB
    Film i Väst AB is a Swedish regional film fund and production company based in Trollhättan, known for co-producing numerous internationally acclaimed films and TV series.
  • E. Norsk Film
    Norsk Film was a major Norwegian film production company that played a central role in the country’s cinema industry throughout much of the 20th century.
  • 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_69d85cd53a7c819080f5b9042c4c199e completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e03fad723481908d2aa33e8f065f2f completed April 16, 2026, 1:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff3660fc6c81908caf1729260a8338 completed May 9, 2026, 1:28 p.m.
Created at: April 10, 2026, 3:49 a.m.