Triple
T10795595
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lee County, Virginia |
E254695
|
entity |
| Predicate | borderTypeWithTennessee |
P95523
|
FINISHED |
| Object | land border |
—
|
LITERAL 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: land border | Statement: [Lee County, Virginia, borderTypeWithTennessee, land border]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: borderTypeWithTennessee Context triple: [Lee County, Virginia, borderTypeWithTennessee, land border]
-
A.
borderDirectionFromKentucky
Indicates the compass direction in which another region lies relative to Kentucky along their shared border.
-
B.
borderTypeWithMongolia
Indicates the specific nature or classification of the border that an entity shares with Mongolia.
-
C.
borderTypeWithTurkmenistan
Indicates the type or nature of the border relationship that an entity has with Turkmenistan.
-
D.
borderRegion
Indicates a region that lies along or near the boundary separating two distinct geographic or political areas.
-
E.
borderTypeWithYemen
Indicates the type or nature of the border that exists between a given entity and Yemen.
- F. None of above. chosen
Provenance (4 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_69d6aa61c15c8190a1839550c56e75e1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d733321dd881909dcd4224dfa9822a |
completed | April 9, 2026, 5:03 a.m. |
| PD | Predicate disambiguation | batch_69d6f3188f00819094ee8d65b187a333 |
completed | April 9, 2026, 12:30 a.m. |
| PDg | Predicate description generation | batch_69d6fa334b8c819082eaf8537084c323 |
completed | April 9, 2026, 1 a.m. |
Created at: April 8, 2026, 9:17 p.m.