Triple

T15737242
Position Surface form Disambiguated ID Type / Status
Subject South Central Wisconsin E381504 entity
Predicate containsCity P294 FINISHED
Object Monroe, Wisconsin E1160617 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: Monroe, Wisconsin | Statement: [South Central Wisconsin, containsCity, Monroe, Wisconsin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Monroe, Wisconsin
Context triple: [South Central Wisconsin, containsCity, Monroe, Wisconsin]
  • A. Monroe, Wisconsin chosen
    Monroe, Wisconsin is a small city in southern Wisconsin known as the "Swiss Cheese Capital of the USA" for its strong Swiss heritage and cheese-making tradition.
  • B. Monona, Wisconsin
    Monona, Wisconsin is a small suburban city located on the shores of Lake Monona just southeast of downtown Madison.
  • C. Washington, Wisconsin
    Washington, Wisconsin is a small town located in Eau Claire County in the western part of the U.S. state of Wisconsin.
  • D. 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.
  • E. West Bend, Wisconsin
    West Bend, Wisconsin is a small city in southeastern Wisconsin known for its historic downtown, access to Kettle Moraine natural areas, and role as a regional commercial and cultural center.
  • 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_69d86d9cdb648190bf3171be0bd7d872 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e04fd6eb888190b7a9b07b76e62c0d completed April 16, 2026, 2:56 a.m.
NED1 Entity disambiguation (via context triple) batch_6a00cfb95b348190a006f699c01e85ce completed May 10, 2026, 6:34 p.m.
Created at: April 10, 2026, 4:46 a.m.