Triple

T16324627
Position Surface form Disambiguated ID Type / Status
Subject North Central Florida E396380 entity
Predicate containsCounty P5971 FINISHED
Object Levy County E377614 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: Levy County | Statement: [North Central Florida, containsCounty, Levy County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Levy County
Context triple: [North Central Florida, containsCounty, Levy County]
  • A. Levy County chosen
    Levy County is a rural county in Florida known for its Gulf Coast shoreline, small towns, and natural springs and forests.
  • B. Thomas County
    Thomas County is a county in southern Georgia, United States, known for its historic city of Thomasville and its blend of agricultural and cultural heritage.
  • C. Suwannee County
    Suwannee County is a rural county in northern Florida known for the Suwannee River, agriculture, and small-town communities.
  • D. Lee County
    Lee County is a county in eastern Alabama known for being home to the city of Auburn and Auburn University.
  • E. Lee County
    Lee County is a county in northern Illinois known for its largely rural landscape, small towns, and agricultural economy.
  • 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_69d87f255b788190a400eba031dd85d8 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e296b8fe988190adee72b23246052f completed April 17, 2026, 8:23 p.m.
NED1 Entity disambiguation (via context triple) batch_6a01c927c7c4819091046c686600db9f completed May 11, 2026, 12:18 p.m.
Created at: April 10, 2026, 5:06 a.m.