Triple

T13548208
Position Surface form Disambiguated ID Type / Status
Subject Anywhere but Here (1999 film) E323569 entity
Predicate settingLocation P40 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: [Anywhere but Here (1999 film), settingLocation, Wisconsin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wisconsin
Context triple: [Anywhere but Here (1999 film), settingLocation, 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 (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_69d8076776248190bdf0d4fa1f85a5fc completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbafdcecf481909999a173b32a58cd completed April 12, 2026, 2:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69f76bae316081909e048ead31a9575a completed May 3, 2026, 3:37 p.m.
Created at: April 9, 2026, 9:45 p.m.