Triple
T5740800
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GRC |
E126608
|
entity |
| Predicate | uniformFeature |
P662
|
FINISHED |
| Object | Stetson hat |
E126611
|
NE FINISHED |
How this triple was built (2 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: Stetson hat | Statement: [GRC, uniformFeature, Stetson hat]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stetson hat Context triple: [GRC, uniformFeature, Stetson hat]
-
A.
Stetson hat
chosen
A Stetson hat is a wide-brimmed, high-crowned felt hat traditionally associated with North American cowboys and law enforcement, symbolizing rugged outdoor style and authority.
-
B.
Hoos
Hoos is a nickname for the University of Virginia Cavaliers and their fans, especially in the context of the school’s athletic teams.
-
C.
Yost
Yost is a surname most notably associated with Fielding H. Yost, the pioneering early 20th-century American college football coach.
-
D.
Helms
Helms is the surname of American actor and comedian Ed Helms, known for his roles in The Office and The Hangover film series.
-
E.
Truman Belt
Truman Belt was a person significant enough in local history that the community of Beltsville, Maryland, was named in his honor.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c0083179548190b384b0bf3c08ca4d |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0255f302c819094f97b4defeded07 |
completed | March 22, 2026, 5:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c07e168ebc8190ba8cb1b3e1b074d5 |
completed | March 22, 2026, 11:41 p.m. |
Created at: March 22, 2026, 3:48 p.m.