Triple
T22381728
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | US 151 |
E553290
|
entity |
| Predicate | connectsCity |
P4245
|
FINISHED |
| Object | Verona, Wisconsin |
—
|
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: Verona, Wisconsin | Statement: [US 151, connectsCity, Verona, Wisconsin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Verona, Wisconsin Context triple: [US 151, connectsCity, Verona, Wisconsin]
-
A.
Verona, Wisconsin
chosen
Verona, Wisconsin is a small city near Madison best known as the home of healthcare software giant Epic Systems.
-
B.
Vernon, Wisconsin
Vernon, Wisconsin is a small town located in Iron County in the northern part of the U.S. state of Wisconsin.
-
C.
Waterford, Wisconsin
Waterford, Wisconsin is a small village in Racine County known for its rural charm, proximity to the Fox River, and tight-knit community.
-
D.
Watertown, Wisconsin
Watertown, Wisconsin is a small city in southeastern Wisconsin known for its historic downtown, riverside setting, and early German-American heritage.
-
E.
Geneva, Wisconsin
Geneva, Wisconsin is a small resort city in southeastern Wisconsin known for its tourism, historic charm, and recreational activities centered around nearby Geneva Lake.
- 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_69e11e4c03248190a26a5060ea6973ee |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1582cce608190b5324b30f349a3ff |
completed | April 29, 2026, 1 a.m. |
Created at: April 16, 2026, 8:45 p.m.