Triple

T14788286
Position Surface form Disambiguated ID Type / Status
Subject Vidette, Georgia E347584 entity
Predicate hasName P744 FINISHED
Object Vidette, Georgia E347584 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: Vidette, Georgia | Statement: [Vidette, Georgia, hasName, Vidette, Georgia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vidette, Georgia
Context triple: [Vidette, Georgia, hasName, Vidette, Georgia]
  • A. Vidette, Georgia chosen
    Vidette, Georgia is a small unincorporated rural community located in Burke County in the eastern part of the state.
  • B. Darien, Georgia
    Darien, Georgia is a historic coastal city in McIntosh County known for its shrimping industry and scenic marshlands along the Atlantic coast.
  • C. De Soto, Georgia
    De Soto, Georgia is a small rural city located in southwestern Georgia in the United States.
  • D. LaFayette, Georgia
    LaFayette, Georgia is a small city in the northwestern part of the state, serving as a local hub for government, commerce, and community life in the surrounding rural area.
  • E. Screven, Georgia
    Screven, Georgia is a small rural town in southeastern Georgia known for its quiet community and location within Wayne County.
  • 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_69d822e9b9e08190bedcc31a163fda82 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69decaa1e9ec81908d7c26c1c4e43014 completed April 14, 2026, 11:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69febfd1a1b48190b3b69b2841f643a3 completed May 9, 2026, 5:02 a.m.
Created at: April 10, 2026, 1:31 a.m.