Triple

T14821973
Position Surface form Disambiguated ID Type / Status
Subject Night at the Museum: Secret of the Tomb E348472 entity
Predicate mainCharacter P1183 FINISHED
Object Larry Daley E90847 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: Larry Daley | Statement: [Night at the Museum: Secret of the Tomb, mainCharacter, Larry Daley]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Larry Daley
Context triple: [Night at the Museum: Secret of the Tomb, mainCharacter, Larry Daley]
  • A. Larry Daley chosen
    Larry Daley is the bumbling yet good-hearted night guard protagonist of the "Night at the Museum" film series, known for dealing with museum exhibits that magically come to life.
  • B. Brad Daugherty
    Brad Daugherty is a former NBA center best known for his All-Star career with the Cleveland Cavaliers in the late 1980s and early 1990s.
  • C. Darryl Philbin
    Darryl Philbin is a laid-back yet sharp-witted warehouse foreman who becomes a key supporting character and later office employee in the U.S. version of The Office.
  • D. Kevin Daley
    Kevin Daley is one of the children of the late Chicago First Lady Maggie Daley and former Mayor Richard M. Daley.
  • E. Gerald Hagey
    Gerald Hagey was a Canadian academic and administrator best known as the founding president who led the development of the University of Waterloo into a major institution.
  • 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_69d822eb8f588190bf53445e730a934f completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69decfe64328819083ce42704cf0602d completed April 14, 2026, 11:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe389bc06c8190a8269c07677d9c35 completed May 8, 2026, 7:25 p.m.
Created at: April 10, 2026, 1:51 a.m.