Triple
T12529544
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Crosby County |
E299524
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Dickens County |
E278941
|
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: Dickens County | Statement: [Crosby County, borders, Dickens County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dickens County Context triple: [Crosby County, borders, Dickens County]
-
A.
Dickens County
chosen
Dickens County is a sparsely populated rural county in northwestern Texas known for its ranching heritage and wide-open plains.
-
B.
Hendry County
Hendry County is a rural county in southern Florida known for its agricultural economy and small communities near Lake Okeechobee.
-
C.
Walton County
Walton County is a coastal county in the Florida Panhandle known for its white-sand beaches, upscale beach communities, and location along the Gulf of Mexico.
-
D.
Lee County
Lee County is a county in northern Illinois known for its largely rural landscape, small towns, and agricultural economy.
-
E.
Lee County
Lee County is a county in eastern Alabama known for being home to the city of Auburn and Auburn University.
- 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_69d6ada5cdd48190860d9ce30aff69be |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d95469d100819087c83bc55e3ec9ce |
completed | April 10, 2026, 7:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a006ec7a4748190822e66a756bc95b9 |
completed | May 10, 2026, 11:40 a.m. |
Created at: April 8, 2026, 9:57 p.m.