Triple

T21679355
Position Surface form Disambiguated ID Type / Status
Subject Guardian E535058 entity
Predicate musicVideoDirector P4911 FINISHED
Object Bartholomew Cubbins NE NERFINISHED

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: Bartholomew Cubbins | Statement: [Guardian, musicVideoDirector, Bartholomew Cubbins]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bartholomew Cubbins
Context triple: [Guardian, musicVideoDirector, Bartholomew Cubbins]
  • A. Bartholomew Cubbins chosen
    Bartholomew Cubbins is a humble young boy from Dr. Seuss’s stories, best known as the protagonist of “The 500 Hats of Bartholomew Cubbins,” where he magically grows endless hats despite a king’s orders.
  • B. Samuel Butts
    Samuel Butts was an American military officer and local hero after whom Butts County, Georgia, was named in recognition of his service.
  • C. Jeremiah Biggs
    Jeremiah Biggs is the son of Reverend Henry Biggs, a clergyman known in his community for his religious leadership.
  • D. Eugene Tackleberry
    Eugene Tackleberry is a gun-obsessed, gung-ho police officer character from the "Police Academy" comedy film series, known for his extreme enthusiasm for weapons and law enforcement.
  • E. Harry Betts
    Harry Betts was an American jazz trombonist, composer, and arranger known for his work in film and television scores.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0c469b6ec8190aee4cadd1527db91 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69ef8a11ce548190aaff404aed6a76cd completed April 27, 2026, 4:08 p.m.
Created at: April 16, 2026, 6:43 p.m.