Triple
T10746520
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Calhoun County, South Carolina |
E253462
|
entity |
| Predicate | ISOCode |
P208
|
FINISHED |
| Object | US-SC |
E22331
|
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: US-SC | Statement: [Calhoun County, South Carolina, ISOCode, US-SC]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: US-SC Context triple: [Calhoun County, South Carolina, ISOCode, US-SC]
-
A.
SC State
SC State is a public, historically Black land-grant university located in Orangeburg, South Carolina.
-
B.
US-MS
US-MS is the ISO 3166-2 code representing the U.S. state of Mississippi.
-
C.
Carolinas
The Carolinas are a region of the southeastern United States comprising the states of North Carolina and South Carolina.
-
D.
South Carolina
chosen
South Carolina is a southeastern U.S. state known for its Atlantic coastline, historic cities like Charleston, and significant role in early American and Civil War history.
-
E.
La Carolina
La Carolina is a town and municipality in the province of Jaén in Andalusia, southern Spain, known historically as one of the New Towns of Sierra Morena founded in the 18th century.
- 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_69d6aa5e51e8819095f06881cecf152e |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d711b85d9c8190a66f536e2f22ed04 |
completed | April 9, 2026, 2:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de230b461c81909a98085079676b95 |
completed | April 14, 2026, 11:20 a.m. |
Created at: April 8, 2026, 9:15 p.m.