Triple

T13799656
Position Surface form Disambiguated ID Type / Status
Subject Filmstaden Bergakungen E331605 entity
Predicate operator P179 FINISHED
Object Filmstaden E331605 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: Filmstaden | Statement: [Filmstaden Bergakungen, operator, Filmstaden]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Filmstaden
Context triple: [Filmstaden Bergakungen, operator, Filmstaden]
  • A. Filmstaden Sergel
    Filmstaden Sergel is a large, modern multiplex cinema in central Stockholm known for showing mainstream films and hosting major movie premieres.
  • B. Filmstaden Bergakungen chosen
    Filmstaden Bergakungen is a large, modern cinema complex in Gothenburg, Sweden, known for its multiple screens and premium movie-going experience.
  • C. City of Film
    City of Film is a UNESCO Creative Cities Network title awarded to cities recognized for their rich film heritage, active film industry, and strong commitment to cinema-related culture and education.
  • D. Film City
    Film City is a major media and entertainment production hub in Noida, housing numerous television studios, news channels, and film production facilities.
  • E. Filmhaus
    Filmhaus is a film production company known for producing the psychological thriller "House of Games."
  • 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_69d81c58feb08190a77bca8bf7d6d20f completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de025ce9148190b23370f6a522ff7a completed April 14, 2026, 9:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7b0893a20819081d4001b8dbc9c36 completed May 3, 2026, 8:31 p.m.
Created at: April 9, 2026, 10:11 p.m.