Triple
T8938623
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rick Kaepernick |
E212839
|
entity |
| Predicate | residence |
P75
|
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: [Rick Kaepernick, residence, Wisconsin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wisconsin Context triple: [Rick Kaepernick, residence, 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.
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.
-
C.
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.
-
D.
Michigan
Michigan is a U.S. state in the Great Lakes region known for its automotive industry, extensive freshwater coastline, and divided Upper and Lower Peninsulas.
-
E.
Michigan
Michigan is a U.S. state in the Great Lakes region known for its automotive industry, extensive freshwater coastline, and manufacturing heritage.
- 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_69ca839694c88190b324ffeb43d23b08 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc66b57a348190979effe4f9998eb7 |
completed | April 1, 2026, 12:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc937ded08190a67fd4457b6458ff |
completed | April 3, 2026, 2:05 p.m. |
Created at: March 30, 2026, 6:58 p.m.