Triple
T22045563
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eula Varner Snopes |
E544753
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Varner |
—
|
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: Varner | Statement: [Eula Varner Snopes, familyName, Varner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Varner Context triple: [Eula Varner Snopes, familyName, Varner]
-
A.
Varner
chosen
Varner is a surname of English origin borne by various notable individuals across fields such as sports, music, and academia.
-
B.
Varnell
Varnell is a small city located in Whitfield County in the northwestern part of the U.S. state of Georgia.
-
C.
Varney
Varney is the surname of American actor and comedian Jim Varney, best known for portraying the character Ernest P. Worrell in films and television.
-
D.
Vaughn
Vaughn is a surname most prominently associated with English film director and producer Matthew Vaughn, known for stylish action and comic-book adaptations.
-
E.
Vaughn
Vaughn is the protagonist of the science fiction novel "Calibre," around whom the story’s central events and conflicts revolve.
- 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_69e11e32445c8190ab97089b48a130bb |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f1282e647481908e054b3ad19e2c15 |
completed | April 28, 2026, 9:35 p.m. |
Created at: April 16, 2026, 8:26 p.m.