Triple

T10394618
Position Surface form Disambiguated ID Type / Status
Subject Catch a Fire (2006 film) E244979 entity
Predicate castMember P1668 FINISHED
Object Marius Weyers E345235 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: Marius Weyers | Statement: [Catch a Fire (2006 film), castMember, Marius Weyers]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marius Weyers
Context triple: [Catch a Fire (2006 film), castMember, Marius Weyers]
  • A. Marius Weyers chosen
    Marius Weyers is a South African actor best known internationally for his role in the film "The Gods Must Be Crazy" and for his extensive work in South African cinema and television.
  • B. Marius de Jonge
    Marius de Jonge is a Dutch biblical scholar known for his influential work on New Testament studies and early Christianity.
  • C. Marius de Vries
    Marius de Vries is a British composer, producer, and arranger known for his innovative work on film soundtracks and collaborations with prominent pop and electronic artists.
  • D. Marius Burger
    Marius Burger is an individual notable enough to be recognized as a distinguished bearer of the surname Burger.
  • E. Jules Cronjager
    Jules Cronjager was an early 20th-century cinematographer and industry figure who helped shape the professional community of American cameramen.
  • 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_69d381b5116081908d85227bab6d3c0c completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4e9ce6bb08190bfeaba98a126526d completed April 7, 2026, 11:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69d795c8271c81908a6b67822050c06d completed April 9, 2026, 12:04 p.m.
Created at: April 6, 2026, 12:06 p.m.