Triple

T16161522
Position Surface form Disambiguated ID Type / Status
Subject Murder Mystery E392190 entity
Predicate starring P1507 FINISHED
Object Dany Boon E1033300 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: Dany Boon | Statement: [Murder Mystery, starring, Dany Boon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dany Boon
Context triple: [Murder Mystery, starring, Dany Boon]
  • A. Dany Boon chosen
    Dany Boon is a French comedian, actor, and filmmaker best known for his popular comedy films such as "Bienvenue chez les Ch'tis."
  • B. Chris Renaud
    Chris Renaud is an American animator and film director best known for co-directing popular animated features such as Despicable Me and The Lorax.
  • C. Andy Robin
    Andy Robin is a screenwriter best known for co-writing the animated comedy film "Bee Movie."
  • D. Olivier Nakache
    Olivier Nakache is a French film director and screenwriter best known for co-directing the internationally acclaimed comedy-drama "The Intouchables."
  • E. Michel Zitt
    Michel Zitt is a prominent French scholar in scientometrics and research evaluation, recognized internationally for his influential contributions to the quantitative study of science and technology.
  • 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_69d87f1d32208190942e4e499a80c18c completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e21e5f0cb48190aae995d88382e055 completed April 17, 2026, 11:49 a.m.
NED1 Entity disambiguation (via context triple) batch_69fff7b33f3481909fe856b8be7d9bcd completed May 10, 2026, 3:12 a.m.
Created at: April 10, 2026, 5:02 a.m.