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.