Triple

T1299903
Position Surface form Disambiguated ID Type / Status
Subject William L. Whittaker E27737 entity
Predicate nickname P55 FINISHED
Object Red
Red is the nickname of William L. "Red" Whittaker, a pioneering American roboticist known for his work in field robotics and autonomous vehicles.
 E147627 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: Red | Statement: [William L. Whittaker, nickname, Red]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Red
Context triple: [William L. Whittaker, nickname, Red]
  • A. Red
    Red is the famous nickname of Arnold "Red" Auerbach, the legendary Boston Celtics coach and executive known for his pivotal role in building an NBA dynasty.
  • B. (RED)
    (RED) is a global charity initiative and brand that partners with companies to raise funds and awareness to fight AIDS and other preventable diseases, particularly in Africa.
  • C. Crimson
    Crimson is the collective name for Harvard University's varsity athletic teams competing in collegiate sports.
  • D. Reddish
    Reddish is a suburban area and former industrial village in the Metropolitan Borough of Stockport, Greater Manchester, England.
  • E. Orange
    Orange is a major French multinational telecommunications company providing mobile, internet, and other digital services across numerous countries.
  • 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: Red
Triple: [William L. Whittaker, nickname, Red]
Generated description
Red is the nickname of William L. "Red" Whittaker, a pioneering American roboticist known for his work in field robotics and autonomous vehicles.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Red
Target entity description: Red is the nickname of William L. "Red" Whittaker, a pioneering American roboticist known for his work in field robotics and autonomous vehicles.
  • A. Red
    Red is the famous nickname of Arnold "Red" Auerbach, the legendary Boston Celtics coach and executive known for his pivotal role in building an NBA dynasty.
  • B. (RED)
    (RED) is a global charity initiative and brand that partners with companies to raise funds and awareness to fight AIDS and other preventable diseases, particularly in Africa.
  • C. Crimson
    Crimson is the collective name for Harvard University's varsity athletic teams competing in collegiate sports.
  • D. Reddish
    Reddish is a suburban area and former industrial village in the Metropolitan Borough of Stockport, Greater Manchester, England.
  • E. Orange
    Orange is a major French multinational telecommunications company providing mobile, internet, and other digital services across numerous countries.
  • 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_69a496d6682881909ba658f1c1e0e2b0 completed March 1, 2026, 7:43 p.m.
NER Named-entity recognition batch_69a4c11314a48190ab4efb8b1acdce50 completed March 1, 2026, 10:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69acacc631b88190853948eeb5f24527 completed March 7, 2026, 10:55 p.m.
NEDg Description generation batch_69acad4b2234819097d94df7812d3b13 completed March 7, 2026, 10:57 p.m.
NED2 Entity disambiguation (via description) batch_69acadf94f0881908b0be66e37b8c04a completed March 7, 2026, 11 p.m.
Created at: March 1, 2026, 7:51 p.m.