Triple
T20861437
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | George Sykes |
E513626
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object | Tardy George |
—
|
NE NERFINISHED |
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: Tardy George | Statement: [George Sykes, nickname, Tardy George]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tardy George Context triple: [George Sykes, nickname, Tardy George]
-
A.
Tardy George
chosen
Tardy George is the nickname of Union Major General George Sykes, known for his deliberate pace and command of the U.S. Army’s V Corps during the American Civil War.
-
B.
Le George
Le George is an upscale contemporary French-Mediterranean restaurant located in Paris’s prestigious Golden Triangle district.
-
C.
Mr. George
Mr. George is a married individual known primarily as the husband of Mrs. George.
-
D.
B-Fine George
B-Fine George is a member of Full Force, the American R&B and hip hop production and performance group known for their influential work in the 1980s and 1990s.
-
E.
Joe St. George
Joe St. George is a violent, abusive character and the primary antagonist in Stephen King’s novel and film adaptation "Dolores Claiborne."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4f5b01081909452f654d2fc3f50 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c3ad3d1c8190be2fe35a85f2447c |
completed | April 21, 2026, 12:24 a.m. |
Created at: April 16, 2026, 12:44 p.m.