Triple
T15752375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wisconsin Highway 42 |
E381877
|
entity |
| Predicate | passesThroughCounty |
P4247
|
FINISHED |
| Object | Brown County |
E487443
|
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: Brown County | Statement: [Wisconsin Highway 42, passesThroughCounty, Brown County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brown County Context triple: [Wisconsin Highway 42, passesThroughCounty, Brown County]
-
A.
Brown County
chosen
Brown County is a county in northeastern Wisconsin that includes the city of Green Bay and operates various public facilities and services for its residents.
-
B.
Brown County
Brown County is a county in northeastern South Dakota that includes the city of Aberdeen as its county seat and primary population center.
-
C.
Wood County
Wood County is a county in central Wisconsin known for its mix of small cities, agricultural areas, and paper industry heritage.
-
D.
Smith County
Smith County is a county in eastern Texas that includes the city of Tyler and serves as a regional hub for healthcare, education, and commerce.
-
E.
Smith County
Smith County is a rural county in central Mississippi known for its small communities, agriculture, and pine forests.
- 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_69d86d9e6b44819085d1f6a969ecb74c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e05030e31081908c307a8dc7067db4 |
completed | April 16, 2026, 2:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff876f005c8190913dec49b839b9f9 |
completed | May 9, 2026, 7:13 p.m. |
Created at: April 10, 2026, 4:47 a.m.