Triple
T10762015
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kent County |
E253850
|
entity |
| Predicate | hasBorderWith |
P224
|
FINISHED |
| Object | Fisher County |
E401683
|
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: Fisher County | Statement: [Kent County, hasBorderWith, Fisher County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fisher County Context triple: [Kent County, hasBorderWith, Fisher County]
-
A.
Fisher County
chosen
Fisher County is a rural county in west-central Texas known for its agricultural economy and small, sparsely populated communities.
-
B.
Mayes County
Mayes County is a county in northeastern Oklahoma known for its mix of small towns, agricultural areas, and recreational lakes.
-
C.
Harding County
Harding County is a sparsely populated rural county in northeastern New Mexico known for its ranching landscape and wide-open high plains.
-
D.
Yoakum County
Yoakum County is a rural county in western Texas known for its agriculture and oil production.
-
E.
Moody County
Moody County is a rural county in eastern South Dakota known for its agricultural landscape and the city of Flandreau as its county seat.
- 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_69d6aa5f54f4819082d0bbcb6f8797e6 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d731a230ac8190920439076aaeb91e |
completed | April 9, 2026, 4:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e23b436efc819080022105e3f5f2e1 |
completed | April 17, 2026, 1:53 p.m. |
Created at: April 8, 2026, 9:16 p.m.