Triple

T5752721
Position Surface form Disambiguated ID Type / Status
Subject Greenberg E126889 entity
Predicate hasCastMember P2308 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: [Greenberg, hasCastMember, Ben Stiller]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ben Stiller
Context triple: [Greenberg, hasCastMember, 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_69c00832aedc81909899801b141fa3b4 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c0288b580c81909e1289982b106695 completed March 22, 2026, 5:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69c07e3e71988190a938a6d175023028 completed March 22, 2026, 11:41 p.m.
Created at: March 22, 2026, 3:48 p.m.