Triple
T22192786
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wood County, Wisconsin |
E548470
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object |
Hansen, Wisconsin
Hansen, Wisconsin is a small rural town located in Wood County in the central part of the state.
|
E1526661
|
NE FINISHED |
How this triple was built (4 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: Hansen, Wisconsin | Statement: [Wood County, Wisconsin, hasTown, Hansen, Wisconsin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hansen, Wisconsin Context triple: [Wood County, Wisconsin, hasTown, Hansen, Wisconsin]
-
A.
Hudson, Wisconsin
Hudson, Wisconsin is a small city in western Wisconsin known as a scenic St. Croix River community with a historic downtown and popular recreational waterfront.
-
B.
Elkhorn, Wisconsin
Elkhorn, Wisconsin is a small city in southeastern Wisconsin known as the administrative and commercial hub of Walworth County.
-
C.
Hawthorne, Wisconsin
Hawthorne, Wisconsin is a small rural town in northwestern Wisconsin known for its forests, lakes, and outdoor recreational opportunities.
-
D.
Hewitt, Wisconsin
Hewitt, Wisconsin is a small rural village located in Wood County in the central part of the state.
-
E.
Sturtevant, Wisconsin
Sturtevant, Wisconsin is a small village in Racine County known for its industrial base, residential communities, and regional rail connectivity between Milwaukee and Chicago.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Hansen, Wisconsin Triple: [Wood County, Wisconsin, hasTown, Hansen, Wisconsin]
Generated description
Hansen, Wisconsin is a small rural town located in Wood County in the central part of the state.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hansen, Wisconsin Target entity description: Hansen, Wisconsin is a small rural town located in Wood County in the central part of the state.
-
A.
Hudson, Wisconsin
Hudson, Wisconsin is a small city in western Wisconsin known as a scenic St. Croix River community with a historic downtown and popular recreational waterfront.
-
B.
Elkhorn, Wisconsin
Elkhorn, Wisconsin is a small city in southeastern Wisconsin known as the administrative and commercial hub of Walworth County.
-
C.
Hawthorne, Wisconsin
Hawthorne, Wisconsin is a small rural town in northwestern Wisconsin known for its forests, lakes, and outdoor recreational opportunities.
-
D.
Hewitt, Wisconsin
Hewitt, Wisconsin is a small rural village located in Wood County in the central part of the state.
-
E.
Sturtevant, Wisconsin
Sturtevant, Wisconsin is a small village in Racine County known for its industrial base, residential communities, and regional rail connectivity between Milwaukee and Chicago.
- F. None of above. chosen
Provenance (5 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_69e11e3e0c7c8190b30d278845e2497e |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12ae49ec881908fa42446b19e3f2d |
completed | April 28, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0ab64b13308190a2f1b8e51abbc9ba |
completed | May 18, 2026, 6:48 a.m. |
| NEDg | Description generation | batch_6a0ab6dce3848190838b858dc021b681 |
completed | May 18, 2026, 6:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0ab7ca94308190a327779ffa509d59 |
completed | May 18, 2026, 6:55 a.m. |
Created at: April 16, 2026, 8:35 p.m.