Triple

T22092849
Position Surface form Disambiguated ID Type / Status
Subject Domino (2019 film) E545951 entity
Predicate writer P1360 FINISHED
Object Petter Skavlan 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: Petter Skavlan | Statement: [Domino (2019 film), writer, Petter Skavlan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Petter Skavlan
Context triple: [Domino (2019 film), writer, Petter Skavlan]
  • A. Petter Skavlan chosen
    Petter Skavlan is a Norwegian screenwriter best known for writing the Oscar-nominated historical adventure film "Kon-Tiki" (2012).
  • B. Torbjørn Sikkeland
    Torbjørn Sikkeland was a Norwegian-American physicist known for his role in the discovery of the synthetic element lawrencium at Lawrence Berkeley National Laboratory.
  • C. Pål Røed
    Pål Røed is a Norwegian film producer known for his work on international productions, including the crime thriller "The Snowman" (2017).
  • D. Jørgen Løvland
    Jørgen Løvland was a Norwegian statesman and educator who served as Prime Minister and held several key ministerial posts during the early years of Norway’s independence.
  • E. Geir Karlsen
    Geir Karlsen is a Norwegian business executive best known for leading the low-cost airline Norwegian Air Shuttle through a major financial restructuring and recovery.
  • 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_69e11e36d03c8190a83a1ba802b7231b completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f128e6b1d881909bf0f4a52199354c completed April 28, 2026, 9:38 p.m.
Created at: April 16, 2026, 8:29 p.m.