Triple

T2675287
Position Surface form Disambiguated ID Type / Status
Subject Palmetto, Georgia E56443 entity
Predicate hasCounty P285 FINISHED
Object Coweta County E105252 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: Coweta County | Statement: [Palmetto, Georgia, hasCounty, Coweta County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Coweta County
Context triple: [Palmetto, Georgia, hasCounty, Coweta County]
  • A. Coweta County chosen
    Coweta County is a county in west-central Georgia, part of the Atlanta metropolitan area, known for its historic communities and growing suburban population.
  • B. Cole County
    Cole County was the former name of what is now Union County in the southeastern part of South Dakota.
  • C. Cleveland County
    Cleveland County is a county in southwestern North Carolina that forms part of the greater Charlotte metropolitan region.
  • D. Lea County
    Lea County is a largely rural, oil- and gas-producing county in southeastern New Mexico known for its energy industry and agricultural activities.
  • E. Barton County
    Barton County is a rural county in southwestern Missouri, United States, known as the birthplace of President Harry S. Truman.
  • 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_69ab4a4b13fc81909dfdb3f23da46832 completed March 6, 2026, 9:42 p.m.
NER Named-entity recognition batch_69abd9b3530c819093942cc985f814ef completed March 7, 2026, 7:54 a.m.
NED1 Entity disambiguation (via context triple) batch_69afa0638a9c8190b48ca5aa56eb66ff completed March 10, 2026, 4:38 a.m.
Created at: March 6, 2026, 9:54 p.m.