Triple
T14475240
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boston, Georgia |
E358951
|
entity |
| Predicate | county |
P75
|
FINISHED |
| Object | Thomas County |
E431628
|
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: Thomas County | Statement: [Boston, Georgia, county, Thomas County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thomas County Context triple: [Boston, Georgia, county, Thomas County]
-
A.
Thomas County
chosen
Thomas County is a county in southern Georgia, United States, known for its historic city of Thomasville and its blend of agricultural and cultural heritage.
-
B.
Lee County
Lee County is a county in northern Illinois known for its largely rural landscape, small towns, and agricultural economy.
-
C.
Lee County
Lee County is a county in eastern Alabama known for being home to the city of Auburn and Auburn University.
-
D.
Lee County
Lee County is a coastal county on Florida’s Gulf Coast known for its beaches, barrier islands, and the city of Fort Myers.
-
E.
Levy County
Levy County is a rural county in Florida known for its Gulf Coast shoreline, small towns, and natural springs and forests.
- 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_69d827966698819082e140837737501d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de91fc1fc48190842b09aa03ba79f8 |
completed | April 14, 2026, 7:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00a4fffa7c81909bcc833b44ddf66f |
completed | May 10, 2026, 3:32 p.m. |
Created at: April 10, 2026, 1:20 a.m.