Triple
T17389065
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brant County |
E422766
|
entity |
| Predicate | adjacentTo |
P224
|
FINISHED |
| Object | Oxford County |
—
|
NE NERFINISHED |
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: Oxford County | Statement: [Brant County, adjacentTo, Oxford County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oxford County Context triple: [Brant County, adjacentTo, Oxford County]
-
A.
Oxford County
Oxford County is a largely rural county in western Maine known for its small towns, forests, and outdoor recreation areas.
-
B.
Oxford County
chosen
Oxford County is a regional municipality in southwestern Ontario, Canada, known for its agricultural communities and the city of Woodstock as its largest urban center.
-
C.
Windsor County
Windsor County is a county in southeastern Vermont known for its historic towns, rural landscapes, and role as a regional center in the Upper Connecticut River Valley.
-
D.
Auglaize County
Auglaize County is a county in western Ohio known for its agricultural communities and its county seat, Wapakoneta.
-
E.
Lebanon County
Lebanon County is a county in south-central Pennsylvania known for its mix of rural farmland, small towns, and historical communities such as the city of Lebanon.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889d710288190bf0f4762801fefae |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a8c718c81909cb20749aaf12897 |
completed | April 19, 2026, 2:14 a.m. |
Created at: April 10, 2026, 5:45 a.m.