Triple
T10243430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Allegany County, Maryland |
E243655
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | West Virginia |
E24143
|
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: West Virginia | Statement: [Allegany County, Maryland, borders, West Virginia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: West Virginia Context triple: [Allegany County, Maryland, borders, West Virginia]
-
A.
West Virginia
chosen
West Virginia is a landlocked, mountainous U.S. state in the Appalachian region, known for its coal mining history, outdoor recreation, and distinct cultural heritage.
-
B.
La Virginia
La Virginia is a municipality in western Colombia known for its location along the Cauca River and its role as a commercial and transport hub in the Risaralda Department.
-
C.
WV
WV is the postcode area covering Wolverhampton and surrounding parts of the West Midlands in England.
-
D.
Virginia
Virginia is a small community located within the town of Georgina in Ontario, Canada.
-
E.
Virginia
Virginia is a coastal township in Montserrado County, Liberia, known for its beaches and proximity to the capital, Monrovia.
- 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_69d381b0f97c819085c9b45799a5fb7c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d22a76188190a73df23bfb08eb3d |
completed | April 7, 2026, 9:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6f7936ce4819087f07df2c7a76282 |
completed | April 9, 2026, 12:49 a.m. |
Created at: April 6, 2026, 11:25 a.m.