Triple
T20276306
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GA-02 |
E503025
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Vienna, Georgia |
—
|
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: Vienna, Georgia | Statement: [GA-02, hasCity, Vienna, Georgia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vienna, Georgia Context triple: [GA-02, hasCity, Vienna, Georgia]
-
A.
Vienna, Georgia
chosen
Vienna, Georgia is a small city in central Georgia that serves as the administrative and commercial hub of Dooly County.
-
B.
Geneva, Georgia
Geneva, Georgia is a small rural town located in west-central Georgia in the United States.
-
C.
Dublin, Georgia
Dublin, Georgia is a small city in Laurens County known as a regional hub in central Georgia with a historic downtown and annual St. Patrick’s Festival.
-
D.
Findlay, Georgia
Findlay, Georgia is a small unincorporated rural community located in Dooly County in the U.S. state of Georgia.
-
E.
Washington, Georgia
Washington, Georgia is a historic small city in Wilkes County known for its well-preserved antebellum architecture and role in early American and Civil War history.
- 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_69e0b4b0e79c8190bd61f22ef1329fa8 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e675e3df68819096fb859bc92a0da1 |
completed | April 20, 2026, 6:52 p.m. |
Created at: April 16, 2026, 10:32 a.m.