Triple
T17820765
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rice County |
E444974
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Steele County |
—
|
NE NERFINISHED |
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: Steele County | Statement: [Rice County, borders, Steele County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Steele County Context triple: [Rice County, borders, Steele County]
-
A.
Steele County
Steele County is a rural county in eastern North Dakota known for its agricultural landscape and small, close-knit communities.
-
B.
Steele County
chosen
Steele County is a county in southern Minnesota known for its agricultural economy and county seat, Owatonna.
-
C.
Fillmore County
Fillmore County is a rural county in southeastern Minnesota known for its rolling farmland, small towns, and karst landscapes with caves and sinkholes.
-
D.
Dodge County
Dodge County is a rural county in central Georgia, United States, known for its agricultural landscape and small-town communities such as its county seat, Eastman.
-
E.
Dodge County
Dodge County is a largely rural county in southeastern Wisconsin known for its agricultural landscape, small towns, and proximity to the Milwaukee metropolitan area.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9f0de78819099395b14db75a8a6 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e48910eb8881908db8ec08e2752d7d |
completed | April 19, 2026, 7:49 a.m. |
Created at: April 10, 2026, 10:15 a.m.