Triple

T5807051
Position Surface form Disambiguated ID Type / Status
Subject Dooly County E128770 entity
Predicate hasCity P316 FINISHED
Object Vienna, Georgia E548646 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: Vienna, Georgia | Statement: [Dooly County, hasCity, Vienna, Georgia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vienna, Georgia
Context triple: [Dooly County, hasCity, Vienna, Georgia]
  • A. Vienna, Georgia chosen
    Vienna, Georgia is a small city in central Georgia that serves as the administrative and commercial hub of Dooly County.
  • B. Geneva, Georgia
    Geneva, Georgia is a small rural town located in west-central Georgia in the United States.
  • C. Dublin, Georgia
    Dublin, Georgia is a small city in Laurens County known as a regional hub in central Georgia with a historic downtown and annual St. Patrick’s Festival.
  • D. Findlay, Georgia
    Findlay, Georgia is a small unincorporated rural community located in Dooly County in the U.S. state of Georgia.
  • E. Washington, Georgia
    Washington, Georgia is a historic small city in Wilkes County known for its well-preserved antebellum architecture and role in early American and Civil War history.
  • 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_69c00846a0d881909e46841f8e156b64 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c02b17417081908779741b9bfbb720 completed March 22, 2026, 5:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0b0dc78a481908fb97c88b2f642fc completed March 23, 2026, 3:17 a.m.
Created at: March 22, 2026, 3:52 p.m.