Triple
T8254429
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Auraria |
E193035
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Dahlonega, Georgia |
E42810
|
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: Dahlonega, Georgia | Statement: [Auraria, locatedNear, Dahlonega, Georgia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dahlonega, Georgia Context triple: [Auraria, locatedNear, Dahlonega, Georgia]
-
A.
Dahlonega, Georgia
chosen
Dahlonega, Georgia is a historic North Georgia mountain town best known as the site of one of the first major U.S. gold rushes and now a popular tourist destination with a preserved 19th-century downtown.
-
B.
Blakely, Georgia
Blakely, Georgia is a small city in southwestern Georgia that serves as the administrative and economic center of Early County.
-
C.
Valdosta, Georgia
Valdosta, Georgia is a small city in southern Georgia known as a regional hub for education, retail, and sports, particularly high school football.
-
D.
De Soto, Georgia
De Soto, Georgia is a small rural city located in southwestern Georgia in the United States.
-
E.
Dalton, Georgia
Dalton, Georgia is a city in northwest Georgia known as a major center of the U.S. carpet and floor-covering industry.
- 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_69ca82dfad9c8190b8cd18fb89f50f40 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb78f8eccc8190b43204bf2f8defc1 |
completed | March 31, 2026, 7:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd354a412c8190963605d177d94a17 |
completed | April 1, 2026, 3:10 p.m. |
Created at: March 30, 2026, 5:48 p.m.