Triple
T14971500
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Phil Garner |
E373330
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Garner |
E5231
|
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: Garner | Statement: [Phil Garner, familyName, Garner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Garner Context triple: [Phil Garner, familyName, Garner]
-
A.
Garner
chosen
Garner is a surname most notably associated with John Nance Garner, the 32nd vice president of the United States under Franklin D. Roosevelt.
-
B.
Bolger
Bolger is a surname most famously associated with American actor and dancer Ray Bolger, who played the Scarecrow in the classic film "The Wizard of Oz."
-
C.
Garlin
Garlin is a surname most notably associated with American comedian and actor Jeff Garlin.
-
D.
Gilchrist
Gilchrist is a surname of Scottish origin borne by various notable individuals in fields such as acting, sports, and public service.
-
E.
Coker
Coker is a surname of English origin borne by various notable individuals across fields such as religion, politics, sports, and the arts.
- 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_69d85ccbbcd48190acb56e7cf104d8ad |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6e59a7c8190a1634a706ea68fda |
completed | April 15, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe8be8af688190832efb00695f8b20 |
completed | May 9, 2026, 1:20 a.m. |
Created at: April 10, 2026, 2:50 a.m.