Triple

T2191365
Position Surface form Disambiguated ID Type / Status
Subject Red Cashion E49868 entity
Predicate nickname P55 FINISHED
Object Red
Red is the nickname of Red Cashion, a well-known former American football official in the National Football League.
 E241412 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: [Red Cashion, nickname, Red]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Red
Context triple: [Red Cashion, 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 the nickname of William L. "Red" Whittaker, a pioneering American roboticist known for his work in field robotics and autonomous vehicles.
  • C. Red
    Red is the nickname of Red Rolfe, an American Major League Baseball third baseman best known for his years with the New York Yankees in the 1930s and 1940s.
  • D. Red
    Red is Taylor Swift’s critically acclaimed 2012 studio album that marked her transition from country to mainstream pop with emotionally charged, genre-blending songs.
  • E. (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.
  • 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: [Red Cashion, nickname, Red]
Generated description
Red is the nickname of Red Cashion, a well-known former American football official in the National Football League.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Red
Target entity description: Red is the nickname of Red Cashion, a well-known former American football official in the National Football League.
  • 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 the nickname of William L. "Red" Whittaker, a pioneering American roboticist known for his work in field robotics and autonomous vehicles.
  • C. Red
    Red is the nickname of Red Rolfe, an American Major League Baseball third baseman best known for his years with the New York Yankees in the 1930s and 1940s.
  • D. Red
    Red is Taylor Swift’s critically acclaimed 2012 studio album that marked her transition from country to mainstream pop with emotionally charged, genre-blending songs.
  • E. (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.
  • 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_69a88aaba3c48190b351cab9b26989ff completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abbf3f5e008190beda3ce5d77209e0 completed March 7, 2026, 6:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae5db0352c8190aa9c4c58460df3a4 completed March 9, 2026, 5:42 a.m.
NEDg Description generation batch_69ae5e60b45c819088bf18ae564f0d1a completed March 9, 2026, 5:45 a.m.
NED2 Entity disambiguation (via description) batch_69ae5ec4a35c8190bffc7a183497e764 completed March 9, 2026, 5:46 a.m.
Created at: March 4, 2026, 7:46 p.m.