Triple

T3933335
Position Surface form Disambiguated ID Type / Status
Subject Larry Daley E90847 entity
Predicate portrayedBy P1507 FINISHED
Object Ben Stiller E72387 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: Ben Stiller | Statement: [Larry Daley, portrayedBy, Ben Stiller]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ben Stiller
Context triple: [Larry Daley, portrayedBy, Ben Stiller]
  • A. Ben Stiller chosen
    Ben Stiller is an American actor, comedian, and filmmaker known for his leading roles in popular comedy films such as "Zoolander," "Meet the Parents," and "There's Something About Mary."
  • 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. Luke Wilson
    Luke Wilson is an American actor known for his roles in films such as "The Royal Tenenbaums," "Old School," and "Legally Blonde."
  • D. Vince Vaughn
    Vince Vaughn is an American actor and comedian known for his roles in hit comedies such as "Wedding Crashers," "Dodgeball," and "Old School."
  • E. Will Ferrell
    Will Ferrell is an American comedian, actor, writer, and producer best known for his work on "Saturday Night Live" and a series of hit comedy films such as "Anchorman" and "Elf."
  • 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_69aed95f26e0819094b0e71974543a19 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeedcab1808190bf653f29062cdddb completed March 9, 2026, 3:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69bd7f58290881908c7622616a829c75 completed March 20, 2026, 5:09 p.m.
Created at: March 9, 2026, 3:23 p.m.