Triple

T3036211
Position Surface form Disambiguated ID Type / Status
Subject Ho-Chunk language E83016 entity
Predicate traditionalRegion P1968 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: [Ho-Chunk language, traditionalRegion, Wisconsin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wisconsin
Context triple: [Ho-Chunk language, traditionalRegion, 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 mix of urban centers and natural landscapes.
  • 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 natural landscapes.
  • D. 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.
  • E. 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.
  • 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_69ad8b2298908190a7cb4e9bdbf064d0 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad9b2bb60c8190b1721f832f2581a1 completed March 8, 2026, 3:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69b1f85f5fa481909785b6c59b4197fb completed March 11, 2026, 11:18 p.m.
Created at: March 8, 2026, 3:01 p.m.