Triple

T21199695
Position Surface form Disambiguated ID Type / Status
Subject Howard Bannister E522419 entity
Predicate associatedWith P37 FINISHED
Object Mr. Larrabee
Mr. Larrabee is a character connected to Howard Bannister in the screwball comedy film "What's Up, Doc?", involved in the film’s web of mistaken identities and chaotic interactions.
 E1471926 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. Larrabee | Statement: [Howard Bannister, associatedWith, Mr. Larrabee]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mr. Larrabee
Context triple: [Howard Bannister, associatedWith, Mr. Larrabee]
  • A. Mr. Dibiasky
    Mr. Dibiasky is a supporting character in the film "Don't Look Up," known primarily as the father of astronomer Kate Dibiasky.
  • B. Mr. Bryles
    Mr. Bryles is the central character in the 1999-set story "Class of 1999," around whom the main events and conflicts revolve.
  • C. Mr. Franks
    Mr. Franks is a music producer best known for his work with the hip-hop collective Legend.
  • D. Mr. Bristal
    Mr. Bristal is a musical artist known for contributing to the project "The Wonderful World of Cease A Leo."
  • E. Mr. Shaibel
    Mr. Shaibel is the quiet, janitor-turned-mentor from "The Queen's Gambit" who introduces Beth Harmon to chess and profoundly shapes her early development as a prodigy.
  • 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. Larrabee
Triple: [Howard Bannister, associatedWith, Mr. Larrabee]
Generated description
Mr. Larrabee is a character connected to Howard Bannister in the screwball comedy film "What's Up, Doc?", involved in the film’s web of mistaken identities and chaotic interactions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mr. Larrabee
Target entity description: Mr. Larrabee is a character connected to Howard Bannister in the screwball comedy film "What's Up, Doc?", involved in the film’s web of mistaken identities and chaotic interactions.
  • A. Mr. Dibiasky
    Mr. Dibiasky is a supporting character in the film "Don't Look Up," known primarily as the father of astronomer Kate Dibiasky.
  • B. Mr. Bryles
    Mr. Bryles is the central character in the 1999-set story "Class of 1999," around whom the main events and conflicts revolve.
  • C. Mr. Franks
    Mr. Franks is a music producer best known for his work with the hip-hop collective Legend.
  • D. Mr. Bristal
    Mr. Bristal is a musical artist known for contributing to the project "The Wonderful World of Cease A Leo."
  • E. Mr. Shaibel
    Mr. Shaibel is the quiet, janitor-turned-mentor from "The Queen's Gambit" who introduces Beth Harmon to chess and profoundly shapes her early development as a prodigy.
  • 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_69e0b51061388190aa03f19700d3ef04 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e7342fe3a08190b7ed2cadf60091a8 completed April 21, 2026, 8:24 a.m.
NED1 Entity disambiguation (via context triple) batch_6a097ecd1d1c819081a3a301701a11ae completed May 17, 2026, 8:39 a.m.
NEDg Description generation batch_6a09807278ac8190ae2835ce6d79a9cc completed May 17, 2026, 8:46 a.m.
NED2 Entity disambiguation (via description) batch_6a09810158948190a9504d50c964efd9 completed May 17, 2026, 8:49 a.m.
Created at: April 16, 2026, 3:17 p.m.