Triple

T22354201
Position Surface form Disambiguated ID Type / Status
Subject Donna Pinciotti E552608 entity
Predicate basedIn P40 FINISHED
Object Wisconsin NE NERFINISHED

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: [Donna Pinciotti, basedIn, Wisconsin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wisconsin
Context triple: [Donna Pinciotti, basedIn, 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 automotive industry, extensive freshwater coastline, and major cities like Detroit and Grand Rapids.
  • 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 forests, rivers, and outdoor recreation areas.
  • D. Michigan
    Michigan is a U.S. state in the Great Lakes region known for its automotive industry, extensive freshwater coastline, and manufacturing heritage.
  • E. Michigan
    Michigan is a U.S. state in the Great Lakes region known for its automotive industry, extensive freshwater coastline, and two distinct peninsulas.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e11e4a0ad08190a385b4d343cf6524 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f157ceb2308190941f6507e605a612 completed April 29, 2026, 12:58 a.m.
Created at: April 16, 2026, 8:44 p.m.