Triple

T15264922
Position Surface form Disambiguated ID Type / Status
Subject Warren County, Georgia E364873 entity
Predicate hasCountySeat P383 FINISHED
Object Warrenton, Georgia E273382 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: Warrenton, Georgia | Statement: [Warren County, Georgia, hasCountySeat, Warrenton, Georgia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Warrenton, Georgia
Context triple: [Warren County, Georgia, hasCountySeat, Warrenton, Georgia]
  • A. Warrenton, Georgia chosen
    Warrenton, Georgia is a small historic city in east-central Georgia that serves as the county seat of Warren County.
  • B. Waynesboro, Georgia
    Waynesboro, Georgia is a small historic city in eastern Georgia known for its agricultural surroundings and role as a local economic and cultural center.
  • C. Winston, Georgia
    Winston, Georgia is an unincorporated community in Douglas County known primarily as a small residential area within the Atlanta metropolitan region.
  • D. Williamson, Georgia
    Williamson, Georgia is a small rural city located in Pike County in the west-central part of the U.S. state of Georgia.
  • E. Arlington, Georgia
    Arlington, Georgia is a small rural city in the southwestern part of the state known historically for its agricultural economy and tight-knit community.
  • 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_69d85a0f08408190b3c3259ae35d79d2 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e00851c5b88190a296b6a105d3ee30 completed April 15, 2026, 9:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69fee5fdc21881909d87062db6fb8fb7 completed May 9, 2026, 7:45 a.m.
Created at: April 10, 2026, 3:14 a.m.