Triple
T5542150
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Central Wisconsin |
E145314
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Juneau County |
E265793
|
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: Juneau County | Statement: [Central Wisconsin, contains, Juneau County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Juneau County Context triple: [Central Wisconsin, contains, Juneau County]
-
A.
Juneau County, Wisconsin
chosen
Juneau County, Wisconsin is a largely rural county in central Wisconsin known for its forests, lakes, and small communities such as Mauston and New Lisbon.
-
B.
Alcona County
Alcona County is a rural county in northeastern Lower Michigan known for its forests, inland lakes, and Lake Huron shoreline.
-
C.
Clearwater County
Clearwater County is a rural county in north-central Idaho known for its forested mountains, rivers, and outdoor recreation opportunities.
-
D.
Ogemaw County
Ogemaw County is a rural county in the northeastern Lower Peninsula of Michigan, known for its forests, lakes, and outdoor recreation opportunities.
-
E.
Forest County
Forest County is a sparsely populated, heavily forested rural county in northwestern Pennsylvania known for its extensive public lands and outdoor recreation.
- 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_69c008fa64888190adae56c8f9ea4031 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01fc7e26481908cec8d0483170ea5 |
completed | March 22, 2026, 4:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c07d6edb7481908e94de7314ca70cc |
completed | March 22, 2026, 11:38 p.m. |
Created at: March 22, 2026, 3:35 p.m.