Triple
T21447428
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jody Varner |
E529112
|
entity |
| Predicate | hasFamilyName |
P18
|
FINISHED |
| Object |
Varner
Varner is a surname of English origin borne by various notable individuals across fields such as sports, music, and academia.
|
E1484911
|
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: Varner | Statement: [Jody Varner, hasFamilyName, Varner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Varner Context triple: [Jody Varner, hasFamilyName, Varner]
-
A.
Varnell
Varnell is a small city located in Whitfield County in the northwestern part of the U.S. state of Georgia.
-
B.
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.
-
C.
Vaughn
Vaughn is a surname most prominently associated with English film director and producer Matthew Vaughn, known for stylish action and comic-book adaptations.
-
D.
DeVasher
DeVasher is a ruthless head of security for a corrupt law firm in John Grisham’s legal thriller "The Firm."
-
E.
Devorski
Devorski is a surname most notably associated with Paul Devorski, a former National Hockey League referee.
- 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: Varner Triple: [Jody Varner, hasFamilyName, Varner]
Generated description
Varner is a surname of English origin borne by various notable individuals across fields such as sports, music, and academia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Varner Target entity description: Varner is a surname of English origin borne by various notable individuals across fields such as sports, music, and academia.
-
A.
Varnell
Varnell is a small city located in Whitfield County in the northwestern part of the U.S. state of Georgia.
-
B.
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.
-
C.
Vaughn
Vaughn is a surname most prominently associated with English film director and producer Matthew Vaughn, known for stylish action and comic-book adaptations.
-
D.
DeVasher
DeVasher is a ruthless head of security for a corrupt law firm in John Grisham’s legal thriller "The Firm."
-
E.
Devorski
Devorski is a surname most notably associated with Paul Devorski, a former National Hockey League referee.
- F. None of above. chosen
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_69e0c457579481909db68053ed99750c |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9e9d04548819086594c20faa5217d |
completed | April 23, 2026, 9:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09c9054e0c81908f13a2240c200b9e |
completed | May 17, 2026, 1:56 p.m. |
| NEDg | Description generation | batch_6a09c993004c81909cc0224947c178e0 |
completed | May 17, 2026, 1:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09ca14cfcc81908565427b504d815e |
completed | May 17, 2026, 2 p.m. |
Created at: April 16, 2026, 6:06 p.m.