Triple

T3338108
Position Surface form Disambiguated ID Type / Status
Subject Cotton Pickin’ Fair E70188 entity
Predicate location P40 FINISHED
Object Gay, Georgia E13421 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: Gay, Georgia | Statement: [Cotton Pickin’ Fair, location, Gay, Georgia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gay, Georgia
Context triple: [Cotton Pickin’ Fair, location, Gay, Georgia]
  • A. Gay, Georgia chosen
    Gay, Georgia is a small rural town in the U.S. state of Georgia, best known for its biannual Cotton Pickin’ Fair that draws visitors from across the region.
  • B. Gray, Georgia
    Gray, Georgia is a small city in Jones County that serves as a local hub within the central region of the state.
  • C. Valdosta, Georgia
    Valdosta, Georgia is a small city in southern Georgia known as a regional hub for education, retail, and sports, particularly high school football.
  • D. Alvaton, Georgia
    Alvaton, Georgia is an unincorporated rural community located in Meriwether County in the west-central part of the state.
  • E. Geneva, Georgia
    Geneva, Georgia is a small rural town located in west-central Georgia in the United States.
  • 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_69ad85a24f208190bcf83131bfed3521 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb1bc31b4819085f01e0b5a7cbc5d completed March 8, 2026, 5:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69b31a8ad1a8819081d7ad2a48e2c5b9 completed March 12, 2026, 7:56 p.m.
Created at: March 8, 2026, 3:12 p.m.