Triple
T4636260
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thomaston |
E101539
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Upson County |
E17106
|
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: Upson County | Statement: [Thomaston, partOf, Upson County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Upson County Context triple: [Thomaston, partOf, Upson County]
-
A.
Upson County
chosen
Upson County is a county in central Georgia, United States, known for its seat in Thomaston and its mix of rural communities and small-town industry.
-
B.
Upton County
Upton County is a sparsely populated, oil-producing county in western Texas known for its role in the Permian Basin energy region.
-
C.
Cass County
Cass County is a rural county in southwestern Iowa known for its agricultural landscape and small communities.
-
D.
Yates County
Yates County is a rural county in New York State’s Finger Lakes region, known for its vineyards, agriculture, and lakeside communities.
-
E.
Crawford County
Crawford County is a rural county in central Georgia known for its agricultural landscape and small-town communities west of Macon.
- 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_69bd43d2f1c081908cd4b7ec48ecc73d |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd5a60a66c8190b76f3d3a7da1df55 |
completed | March 20, 2026, 2:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdfacba5fc8190bc86157ee5719ced |
completed | March 21, 2026, 1:56 a.m. |
Created at: March 20, 2026, 1:13 p.m.