Triple
T2950024
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flag of Wisconsin |
E79797
|
entity |
| Predicate | text |
P2203
|
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: [Flag of Wisconsin, text, WISCONSIN]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: WISCONSIN Context triple: [Flag of Wisconsin, text, 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.
Minnesota and Wisconsin
Minnesota and Wisconsin are two neighboring U.S. states in the Upper Midwest, known for their abundant lakes, forests, and shared border along the upper Mississippi River.
-
C.
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.
-
D.
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 natural landscapes.
-
E.
Michigan
Michigan is a U.S. state in the Great Lakes region known for its extensive freshwater coastline, automotive industry heritage, and diverse forests and waterways.
- 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_69ad8b1276588190a374a0b12e0f7bdf |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad98f95da88190a036a18f39f221c2 |
completed | March 8, 2026, 3:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b108d7b64c8190a18bff804467fa2c |
completed | March 11, 2026, 6:16 a.m. |
Created at: March 8, 2026, 2:57 p.m.