Triple

T5165173
Position Surface form Disambiguated ID Type / Status
Subject Southwest Georgia E116532 entity
Predicate hasCity P316 FINISHED
Object Valdosta, Georgia E214466 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: Valdosta, Georgia | Statement: [Southwest Georgia, hasCity, Valdosta, Georgia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Valdosta, Georgia
Context triple: [Southwest Georgia, hasCity, Valdosta, Georgia]
  • A. Valdosta, Georgia chosen
    Valdosta, Georgia is a small city in southern Georgia known as a regional hub for education, retail, and sports, particularly high school football.
  • B. Odum, Georgia
    Odum, Georgia is a small town in southeastern Georgia, United States, located in Wayne County.
  • C. Alvaton, Georgia
    Alvaton, Georgia is an unincorporated rural community located in Meriwether County in the west-central part of the state.
  • D. Jakin, Georgia
    Jakin, Georgia is a small rural city in southwestern Georgia known for its agricultural surroundings and close-knit community.
  • E. Vidalia, Georgia
    Vidalia, Georgia is a small city in southeastern Georgia best known as the namesake and primary production area of the famous sweet Vidalia onions.
  • 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_69bd445edb3881909b93b34d260717fc completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd792af4648190934cf2db523f6921 completed March 20, 2026, 4:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69befe5521dc8190b2c6f03faa436529 completed March 21, 2026, 8:23 p.m.
Created at: March 20, 2026, 1:44 p.m.