Triple
T15856840
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Potter County |
E384480
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Moore County |
E376943
|
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: Moore County | Statement: [Potter County, borders, Moore County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Moore County Context triple: [Potter County, borders, Moore County]
-
A.
Moore County
chosen
Moore County is a rural county in the Texas Panhandle known for its agriculture, energy production, and small-town communities.
-
B.
Runnels County
Runnels County is a rural county in west-central Texas known for its agricultural economy and small-town communities.
-
C.
McKenzie County
McKenzie County is a sparsely populated county in western North Dakota known for its oil production, ranching, and access to outdoor recreation along Lake Sakakawea and the Badlands.
-
D.
Suide County
Suide County is an administrative county in northern Shaanxi Province, China, known for its historical significance and location along the middle reaches of the Yellow River.
-
E.
Burke County
Burke County is a largely rural county in eastern Georgia known for its agricultural landscape and small towns such as Sardis and Waynesboro.
- 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_69d86da422088190aac39e32e6c68429 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e14cb08bd081908af2120eb2925441 |
completed | April 16, 2026, 8:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0035450810819092796c556dfa8ed3 |
completed | May 10, 2026, 7:35 a.m. |
Created at: April 10, 2026, 4:50 a.m.