Triple
T439427
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Raleigh–Durham area |
E10081
|
entity |
| Predicate | contains |
P35
|
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: [Raleigh–Durham area, contains, Garner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Garner Context triple: [Raleigh–Durham area, contains, 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.
Greene
Greene is a common English surname borne by numerous notable figures in politics, the military, the arts, and other fields.
-
C.
Graham
Graham is the surname of Elizabeth Arden, the pioneering Canadian-American businesswoman who founded the iconic Elizabeth Arden cosmetics empire.
-
D.
Gordon
Gordon is the middle name of the famed Romantic poet Lord Byron, whose full name is George Gordon Byron.
-
E.
Gorman
Gorman is a surname most notably associated with English actor Burn Gorman, known for his roles in film and television.
- 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_69a2e8465ef481909655c681b01e2986 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2ef2977888190a0590b2c1bd2f5da |
completed | Feb. 28, 2026, 1:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4366eb0588190beb5c43828f8ca45 |
completed | March 1, 2026, 12:51 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.