Triple
T15276233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chippewa River |
E365146
|
entity |
| Predicate | hasCityOnBank |
P7935
|
FINISHED |
| Object | Durand, Wisconsin |
E923308
|
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: Durand, Wisconsin | Statement: [Chippewa River, hasCityOnBank, Durand, Wisconsin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Durand, Wisconsin Context triple: [Chippewa River, hasCityOnBank, Durand, Wisconsin]
-
A.
Durand, Wisconsin
chosen
Durand, Wisconsin is a small city in Pepin County known as a rural Midwestern community along the Chippewa River.
-
B.
Darien, Wisconsin
Darien, Wisconsin is a small rural village located in southeastern Wisconsin within Walworth County.
-
C.
Lindina, Wisconsin
Lindina, Wisconsin is a small rural community located in Juneau County in the central part of the state.
-
D.
Necedah, Wisconsin
Necedah, Wisconsin is a small village in central Wisconsin known as a gateway to nearby wildlife refuges and outdoor recreation areas.
-
E.
Elkhorn, Wisconsin
Elkhorn, Wisconsin is a small city in southeastern Wisconsin known as the administrative and commercial hub of Walworth County.
- 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_69d85a0f08408190b3c3259ae35d79d2 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00952731c8190bf6a5e6e10c95b94 |
completed | April 15, 2026, 9:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fef895f9708190a44ee7ade1c46a7d |
completed | May 9, 2026, 9:04 a.m. |
Created at: April 10, 2026, 3:14 a.m.