Triple

T15047228
Position Surface form Disambiguated ID Type / Status
Subject Madison Metro E379260 entity
Predicate alsoServes P6337 FINISHED
Object Verona, Wisconsin E486225 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: Verona, Wisconsin | Statement: [Madison Metro, alsoServes, Verona, Wisconsin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Verona, Wisconsin
Context triple: [Madison Metro, alsoServes, Verona, Wisconsin]
  • A. Verona, Wisconsin chosen
    Verona, Wisconsin is a small city near Madison best known as the home of healthcare software giant Epic Systems.
  • B. Vernon, Wisconsin
    Vernon, Wisconsin is a small town located in Iron County in the northern part of the U.S. state of Wisconsin.
  • C. Marinette, Wisconsin
    Marinette, Wisconsin is a small industrial city in northeastern Wisconsin on the shore of Green Bay, known historically for shipbuilding and its location opposite Menominee, Michigan.
  • D. Finley, Wisconsin
    Finley, Wisconsin is a small unincorporated community located in rural Juneau County in central Wisconsin.
  • E. Bristol, Wisconsin
    Bristol, Wisconsin is a small village in Kenosha County known for its rural character and location along major regional routes between Lake Geneva and the Chicago–Milwaukee corridor.
  • 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_69d85cd64d108190853797a95c11cc45 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69deda8e64e48190873104a02a676ff3 completed April 15, 2026, 12:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe9de73614819098b7a88624407d0e completed May 9, 2026, 2:37 a.m.
Created at: April 10, 2026, 3 a.m.