Triple
T1175803
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blount County, Alabama |
E25022
|
entity |
| Predicate | largestCity |
P235
|
FINISHED |
| Object | Oneonta, Alabama |
E208211
|
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: Oneonta, Alabama | Statement: [Blount County, Alabama, largestCity, Oneonta, Alabama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oneonta, Alabama Context triple: [Blount County, Alabama, largestCity, Oneonta, Alabama]
-
A.
Oneonta, Alabama
chosen
Oneonta, Alabama is a small city in north-central Alabama known as the administrative and commercial hub of Blount County.
-
B.
Odenville, Alabama
Odenville, Alabama is a small town in central Alabama known for its rural character and location within the Birmingham metropolitan area.
-
C.
Steele, Alabama
Steele, Alabama is a small town in northeastern Alabama known for its rural character and location within St. Clair County.
-
D.
Kingston, Alabama
Kingston, Alabama is an unincorporated rural community located in Shelby County in the central part of the state.
-
E.
Helena, Alabama
Helena, Alabama is a suburban city in central Alabama known for its family-friendly neighborhoods, strong schools, and proximity to Birmingham.
- 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_69a494267b4c819088c97a59182bf56a |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd0d5c288190b597dae0fbe3b43b |
completed | March 1, 2026, 10:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69addf1667908190bdcb6d200577418d |
completed | March 8, 2026, 8:41 p.m. |
Created at: March 1, 2026, 7:45 p.m.