Triple

T20117733
Position Surface form Disambiguated ID Type / Status
Subject Beggar on Horseback E490510 entity
Predicate hasCharacter P2308 FINISHED
Object Mr. Cady
Mr. Cady is a character in the satirical play "Beggar on Horseback," representing the pressures and absurdities of commercialism in the arts.
E1411082 NE FINISHED

How this triple was built (4 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: Mr. Cady | Statement: [Beggar on Horseback, hasCharacter, Mr. Cady]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mr. Cady
Context triple: [Beggar on Horseback, hasCharacter, Mr. Cady]
  • A. Dom Dwyer
    Dom Dwyer is an English-born professional soccer player and forward who has played in Major League Soccer, notably for Sporting Kansas City and Orlando City SC.
  • B. Mr. Dibiasky
    Mr. Dibiasky is a supporting character in the film "Don't Look Up," known primarily as the father of astronomer Kate Dibiasky.
  • C. Mr. McAllister
    Mr. McAllister is a fictional teacher at the elite preparatory school Welton Academy in the film "Dead Poets Society."
  • D. Andrew Cody
    Andrew Cody is an individual known by the nickname "Pope," suggesting a distinctive or notable personal or public persona.
  • E. Mr. Garrett
    Mr. Garrett is a young librarian and the protagonist of M. R. James’s ghost story “The Tractate Middoth,” who becomes entangled in a sinister mystery involving an old book and a haunted inheritance.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Mr. Cady
Triple: [Beggar on Horseback, hasCharacter, Mr. Cady]
Generated description
Mr. Cady is a character in the satirical play "Beggar on Horseback," representing the pressures and absurdities of commercialism in the arts.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Mr. Cady
Target entity description: Mr. Cady is a character in the satirical play "Beggar on Horseback," representing the pressures and absurdities of commercialism in the arts.
  • A. Dom Dwyer
    Dom Dwyer is an English-born professional soccer player and forward who has played in Major League Soccer, notably for Sporting Kansas City and Orlando City SC.
  • B. Mr. Dibiasky
    Mr. Dibiasky is a supporting character in the film "Don't Look Up," known primarily as the father of astronomer Kate Dibiasky.
  • C. Mr. McAllister
    Mr. McAllister is a fictional teacher at the elite preparatory school Welton Academy in the film "Dead Poets Society."
  • D. Andrew Cody
    Andrew Cody is an individual known by the nickname "Pope," suggesting a distinctive or notable personal or public persona.
  • E. Mr. Garrett
    Mr. Garrett is a young librarian and the protagonist of M. R. James’s ghost story “The Tractate Middoth,” who becomes entangled in a sinister mystery involving an old book and a haunted inheritance.
  • F. None of above. chosen

Provenance (5 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_69da62636cc08190982cc71733a17b8d completed April 11, 2026, 3:01 p.m.
NER Named-entity recognition batch_69e6673ab4e08190b76ec742605e103b completed April 20, 2026, 5:49 p.m.
NED1 Entity disambiguation (via context triple) batch_6a08271de03c81908f669f712ca1233a completed May 16, 2026, 8:13 a.m.
NEDg Description generation batch_6a08280f9e70819091682323b55ca855 completed May 16, 2026, 8:17 a.m.
NED2 Entity disambiguation (via description) batch_6a08288d98fc8190a146f43a027682f0 completed May 16, 2026, 8:19 a.m.
Created at: April 11, 2026, 11:30 p.m.