Triple
T22738615
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haralson County, Georgia |
E562343
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object | Waco, 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: Waco, Georgia | Statement: [Haralson County, Georgia, hasTown, Waco, Georgia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Waco, Georgia Context triple: [Haralson County, Georgia, hasTown, Waco, Georgia]
-
A.
Waco, Georgia
chosen
Waco, Georgia is a small community in western Georgia known for its rural character and proximity to the Alabama state line.
-
B.
Colquitt, Georgia
Colquitt, Georgia is a small city in southwest Georgia known as the cultural and economic hub of Miller County.
-
C.
Waverly, Georgia
Waverly, Georgia is a small unincorporated community located in southeastern Georgia near the Atlantic coast.
-
D.
Whigham, Georgia
Whigham, Georgia is a small rural city in the southwestern part of the state known for its tight-knit community and agricultural surroundings.
-
E.
Williamson, Georgia
Williamson, Georgia is a small rural city located in Pike County in the west-central part of the U.S. state of Georgia.
- 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_69e245513a5c81908d5cb471b4fc429d |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17971751081909253984429c8d178 |
completed | April 29, 2026, 3:22 a.m. |
Created at: April 17, 2026, 3:22 p.m.