Triple
T10293129
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red Cashion |
E241412
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object |
Red
Red is the nickname of Red Cashion, a well-known former NFL referee recognized for his exuberant first-down calls.
|
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 Red Rolfe, an American Major League Baseball third baseman best known for his years with the New York Yankees in the 1930s and 1940s.
-
C.
Red
Red is Virgin America’s signature in-flight entertainment system, offering passengers on-demand movies, TV, music, games, and other interactive services.
-
D.
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.
-
E.
Red
Red is a small, unicycle character from Pixar’s early animated short film "Red’s Dream."
- 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 NFL referee recognized for his exuberant first-down calls.
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 NFL referee recognized for his exuberant first-down calls.
-
A.
Red
chosen
Red is the nickname of Red Cashion, a well-known former American football official in the National Football League.
-
B.
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.
-
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 major character from the Pokémon franchise, known as the silent, highly skilled Pokémon Trainer who serves as the protagonist of the original games and a legendary opponent in later titles.
- 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_69d381aaafc08190af475ef58dc16aba |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d2d46fb08190b7694290692e47dc |
completed | April 7, 2026, 9:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d71d1c180481909ca9983e14cbb931 |
completed | April 9, 2026, 3:29 a.m. |
| NEDg | Description generation | batch_69d73182d7548190ac15093aa7001db7 |
completed | April 9, 2026, 4:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d7336c06308190ac72154134a26842 |
completed | April 9, 2026, 5:04 a.m. |
Created at: April 6, 2026, 11:42 a.m.