Triple

T7502580
Position Surface form Disambiguated ID Type / Status
Subject Arnold Jacob Auerbach E177300 entity
Predicate nickname P55 FINISHED
Object Red
Red was the nickname of Arnold Jacob Auerbach, an American professional basketball player and legendary Boston Celtics coach and executive.
E63964 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: [Arnold Jacob Auerbach, nickname, Red]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Red
Context triple: [Arnold Jacob Auerbach, nickname, Red]
  • A. 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.
  • B. 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.
  • C. Red
    Red is the nickname of Red Cashion, a well-known former American football official in the National Football League.
  • D. Red
    "Red" is a stage play by John Logan that dramatizes the life and work of abstract expressionist painter Mark Rothko, particularly his creation of the Seagram Murals.
  • E. Red
    Red is the tough, sharp-tongued Russian matriarch and prison cook from the television series "Orange Is the New Black."
  • 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: [Arnold Jacob Auerbach, nickname, Red]
Generated description
Red was the nickname of Arnold Jacob Auerbach, an American professional basketball player and legendary Boston Celtics coach and executive.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Red
Target entity description: Red was the nickname of Arnold Jacob Auerbach, an American professional basketball player and legendary Boston Celtics coach and executive.
  • A. Red chosen
    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 Red Cashion, a well-known former American football official in the National Football League.
  • 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 the nickname of William L. "Red" Whittaker, a pioneering American roboticist known for his work in field robotics and autonomous vehicles.
  • E. Red
    "Red" is a stage play by John Logan that dramatizes the life and work of abstract expressionist painter Mark Rothko, particularly his creation of the Seagram Murals.
  • F. None of above.

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_69c69f2696688190915a8458f2398211 completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f59be2748190ad8e94179f594e51 completed March 27, 2026, 9:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69c83c9953e88190a1e0e899f2ddf822 completed March 28, 2026, 8:39 p.m.
NEDg Description generation batch_69c83defe434819086bf6d63c8f2675e completed March 28, 2026, 8:45 p.m.
NED2 Entity disambiguation (via description) batch_69c83e531ea881909b6186de9adbccc0 completed March 28, 2026, 8:47 p.m.
Created at: March 27, 2026, 3:44 p.m.