Triple

T20759903
Position Surface form Disambiguated ID Type / Status
Subject KROC E510949 entity
Predicate operator P179 FINISHED
Object Monroe County 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: Monroe County | Statement: [KROC, operator, Monroe County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Monroe County
Context triple: [KROC, operator, Monroe County]
  • A. Monroe County
    Monroe County is a county in central Georgia known for its mix of rural communities, historic towns like Forsyth, and its location along major transportation routes between Atlanta and Macon.
  • B. Monroe County
    Monroe County is a rural county in southern West Virginia known for its scenic Appalachian landscapes, agriculture, and historic small towns.
  • C. Monroe County
    Monroe County is a rural county in south-central Iowa known for its agricultural landscape and small communities such as Melrose.
  • D. Monroe County
    Monroe County is a rural county in southwestern Alabama known historically as the home of Monroeville, the hometown of author Harper Lee and a setting that inspired "To Kill a Mockingbird."
  • E. Monroe County
    Monroe County is a county in the U.S. state of Michigan located along the western shore of Lake Erie, south of Detroit.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide. chosen

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_69e0b4c909ec8190b05987f1639513f6 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c24751688190829f9d836abfb606 completed April 21, 2026, 12:18 a.m.
Created at: April 16, 2026, 12:35 p.m.