Triple
T1491739
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Minnesota Lynx |
E29594
|
entity |
| Predicate | state |
P87
|
FINISHED |
| Object | Minnesota |
E33799
|
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: Minnesota | Statement: [Minnesota Lynx, state, Minnesota]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Minnesota Context triple: [Minnesota Lynx, state, Minnesota]
-
A.
Minnesota
chosen
Minnesota is a U.S. state known for its numerous lakes, cold winters, and vibrant cultural and economic centers like Minneapolis–Saint Paul.
-
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.
Wisconsin
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.
-
D.
North Dakota
North Dakota is a sparsely populated U.S. state known for its Great Plains landscapes, agricultural economy, and significant oil production from the Bakken formation.
-
E.
Michigan
Michigan is a U.S. state in the Great Lakes region known for its extensive freshwater coastline, automotive industry centered in Detroit, and diverse mix of urban centers and natural landscapes.
- 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_69a498da82e08190ba833330d05f380f |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c6c3ace4819081bc2b86ee2486b6 |
completed | March 1, 2026, 11:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae6515d5f881908f89fa1d6377596e |
completed | March 9, 2026, 6:13 a.m. |
Created at: March 1, 2026, 8:12 p.m.