Triple
T20276301
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GA-02 |
E503025
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Dawson, Georgia |
E118552
|
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: Dawson, Georgia | Statement: [GA-02, hasCity, Dawson, Georgia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dawson, Georgia Context triple: [GA-02, hasCity, Dawson, Georgia]
-
A.
Dawson, Georgia
chosen
Dawson, Georgia is a small city in Terrell County known as an agricultural and regional trade center in southwest Georgia.
-
B.
Dawsonville, Georgia, United States
Dawsonville, Georgia, United States, is a small city in north Georgia best known as the hometown of NASCAR legend Bill Elliott and for its strong stock car racing heritage.
-
C.
Damascus, Georgia
Damascus, Georgia is a small rural town located in southwestern Georgia within Early County.
-
D.
Douglas, Georgia
Douglas, Georgia is a small city in south-central Georgia that serves as the county seat of Coffee County and a regional hub for agriculture and industry.
-
E.
Blakely, Georgia
Blakely, Georgia is a small city in southwestern Georgia that serves as the administrative and economic center of Early County.
- 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_69e0b4b0e79c8190bd61f22ef1329fa8 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e675e3df68819096fb859bc92a0da1 |
completed | April 20, 2026, 6:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a085a21233881908fb835541ae88c70 |
completed | May 16, 2026, 11:50 a.m. |
Created at: April 16, 2026, 10:32 a.m.