Triple

T16964565
Position Surface form Disambiguated ID Type / Status
Subject Red Schoendienst E411509 entity
Predicate nickname P55 FINISHED
Object Red
Red is the nickname of Hall of Fame Major League Baseball second baseman and longtime St. Louis Cardinals player and manager Red Schoendienst.
E1242559 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 Schoendienst, nickname, Red]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Red
Context triple: [Red Schoendienst, 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 one of the main playable heroes in the run-and-gun video game Gunstar Heroes, known for fast-paced combat and cooperative action.
  • D. Red
    Red is a small, unicycle character from Pixar’s early animated short film "Red’s Dream."
  • E. Red
    "Red" is a 2010 action-comedy film about retired black-ops agents forced back into the field, known for its ensemble cast led by Bruce Willis, Helen Mirren, Morgan Freeman, and John Malkovich.
  • 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 Schoendienst, nickname, Red]
Generated description
Red is the nickname of Hall of Fame Major League Baseball second baseman and longtime St. Louis Cardinals player and manager Red Schoendienst.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Red
Target entity description: Red is the nickname of Hall of Fame Major League Baseball second baseman and longtime St. Louis Cardinals player and manager Red Schoendienst.
  • 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 the nickname of Red Cashion, a well-known former American football official in the National Football League.
  • C. 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.
  • D. Red
    Red is the nickname of Red Garland, a renowned American jazz pianist known for his work with the Miles Davis Quintet and his influential trio recordings.
  • E. Red
    Red is the nickname of William L. "Red" Whittaker, a pioneering American roboticist known for his work in field robotics and autonomous vehicles.
  • 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_69d886c9c9d481909afe222093641cae completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d0a2cda88190bd574a869f0e43e9 completed April 18, 2026, 6:42 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00d46cb56481908c2bc6648a12fbcf completed May 10, 2026, 6:54 p.m.
NEDg Description generation batch_6a00d4f1bfa48190903bedc43ed6db75 completed May 10, 2026, 6:56 p.m.
NED2 Entity disambiguation (via description) batch_6a00d59b96108190a0e55f01529a0b64 completed May 10, 2026, 6:59 p.m.
Created at: April 10, 2026, 5:31 a.m.