Triple

T14955949
Position Surface form Disambiguated ID Type / Status
Subject Bernard "Beanie" Campbell E372925 entity
Predicate portrayedBy P1507 FINISHED
Object Vince Vaughn E257907 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: Vince Vaughn | Statement: [Bernard "Beanie" Campbell, portrayedBy, Vince Vaughn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vince Vaughn
Context triple: [Bernard "Beanie" Campbell, portrayedBy, Vince Vaughn]
  • A. Vince Vaughn chosen
    Vince Vaughn is an American actor and comedian known for his roles in hit comedies such as "Wedding Crashers," "Dodgeball," and "Old School."
  • B. Owen Wilson
    Owen Wilson is an American actor and screenwriter known for his laid-back charm and roles in popular comedies and adventure films such as "Wedding Crashers," "Zoolander," and "Midnight in Paris."
  • C. Chris Penn
    Chris Penn was an American character actor known for his roles in films such as "Reservoir Dogs," "Footloose," and "True Romance."
  • D. Kevin James
    Kevin James is an American actor and comedian best known for starring in the sitcom "The King of Queens" and films such as "Paul Blart: Mall Cop."
  • E. Luke Wilson
    Luke Wilson is an American actor known for his roles in films such as "The Royal Tenenbaums," "Old School," and "Legally Blonde."
  • 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_69d85cca979481908747d2a81eba1cea completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded6cc73848190ac181782b20dc838 completed April 15, 2026, 12:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe968f25e08190bfbf7a3541f79add completed May 9, 2026, 2:06 a.m.
Created at: April 10, 2026, 2:40 a.m.