Triple
T418342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amish |
E8043
|
entity |
| Predicate | significantPopulationIn |
P1898
|
FINISHED |
| Object | Wisconsin |
E16627
|
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: Wisconsin | Statement: [Amish, significantPopulationIn, Wisconsin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wisconsin Context triple: [Amish, significantPopulationIn, Wisconsin]
-
A.
Wisconsin
chosen
Wisconsin is a U.S. state in the Upper Midwest known for its dairy industry, Great Lakes shorelines, and mix of rural landscapes and industrial cities.
-
B.
Iowa
Iowa is a Midwestern U.S. state known for its extensive agriculture, especially corn and soybean production, and its role in national politics through the Iowa caucuses.
-
C.
Minnesota
Minnesota is a U.S. state known for its numerous lakes, cold winters, and vibrant cultural and economic centers like Minneapolis–Saint Paul.
-
D.
Illinois
Illinois is a Midwestern U.S. state known for its major metropolis Chicago, diverse economy, and significant political and transportation influence.
-
E.
Indiana
Indiana is a U.S. state known for its manufacturing base, rich agricultural land, and iconic events like the Indianapolis 500.
- 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_69a2e7f1d1bc81909cf2dc9754a3c334 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ee9059248190ba901680431914b5 |
completed | Feb. 28, 2026, 1:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acbac916188190b850b247232887a2 |
completed | March 7, 2026, 11:54 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.