Triple
T6310194
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ambridge |
E141478
|
entity |
| Predicate | county |
P75
|
FINISHED |
| Object | Beaver County |
E20430
|
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: Beaver County | Statement: [Ambridge, county, Beaver County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beaver County Context triple: [Ambridge, county, Beaver County]
-
A.
Beaver County
chosen
Beaver County is a county in western Pennsylvania that forms part of the greater Pittsburgh metropolitan area.
-
B.
Beaver County
Beaver County is a rural county in southwestern Utah known for its mountainous terrain, outdoor recreation, and the county seat of Beaver.
-
C.
Beaver County
Beaver County is a sparsely populated county in the Oklahoma Panhandle known for its agricultural economy and wide-open High Plains landscape.
-
D.
Park County
Park County is a rural county in southwestern Montana known for its proximity to Yellowstone National Park and its scenic mountain landscapes.
-
E.
Adams County
Adams County is a rural county in central Wisconsin known for its forests, lakes, and outdoor recreation.
- 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_69c008d00efc8190a36c05b4b4a3bf4b |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0648074b081908ba661651ba705a7 |
completed | March 22, 2026, 9:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6040337908190a10f7cd9c1264267 |
completed | March 27, 2026, 4:13 a.m. |
Created at: March 22, 2026, 4:28 p.m.