Triple

T19868223
Position Surface form Disambiguated ID Type / Status
Subject Legally Blonde E477445 entity
Predicate character P662 FINISHED
Object Professor Callahan 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: Professor Callahan | Statement: [Legally Blonde, character, Professor Callahan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Professor Callahan
Context triple: [Legally Blonde, character, Professor Callahan]
  • A. Professor Callahan chosen
    Professor Callahan is a central, morally ambiguous law professor and antagonist in the Broadway musical adaptation of "Legally Blonde."
  • B. Professor Burris
    Professor Burris is the skeptical psychology professor and narrator of B.F. Skinner’s utopian novel "Walden Two," through whose perspective the experimental community is explored and critiqued.
  • C. Professor Porter
    Professor Porter is a bumbling yet kind-hearted British academic and explorer who appears as Jane's father in various Tarzan film adaptations.
  • D. Professor Thorton
    Professor Thorton is a Marvel Comics scientist associated with the Weapon Plus program, known for his role in creating and experimenting on super-soldiers like Wolverine.
  • E. Professor LeBlanc
    Professor LeBlanc is a recurring comedic character from the classic American radio and television series "The Jack Benny Program."
  • 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_69d8e51e7d948190aedbcd6c30361c39 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e658a168288190a2fbb735d1fd30a8 completed April 20, 2026, 4:47 p.m.
Created at: April 10, 2026, 1:51 p.m.