Triple

T14991927
Position Surface form Disambiguated ID Type / Status
Subject Wolf Island National Wildlife Refuge E373855 entity
Predicate nearestCity P350 FINISHED
Object Darien, Georgia E377640 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: Darien, Georgia | Statement: [Wolf Island National Wildlife Refuge, nearestCity, Darien, Georgia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Darien, Georgia
Context triple: [Wolf Island National Wildlife Refuge, nearestCity, Darien, Georgia]
  • A. Darien, Georgia chosen
    Darien, Georgia is a historic coastal city in McIntosh County known for its shrimping industry and scenic marshlands along the Atlantic coast.
  • B. Vidette, Georgia
    Vidette, Georgia is a small unincorporated rural community located in Burke County in the eastern part of the state.
  • C. Screven, Georgia
    Screven, Georgia is a small rural town in southeastern Georgia known for its quiet community and location within Wayne County.
  • D. Folkston, Georgia
    Folkston, Georgia is a small city in southeastern Georgia known as a gateway to the Okefenokee Swamp and a popular spot for train watching.
  • E. Union Point, Georgia
    Union Point, Georgia is a small historic city in Greene County known for its railroad heritage and preserved 19th-century architecture.
  • 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_69d85ccc84388190aa151e5173370c8d completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded715db408190b44e8a8452c79764 completed April 15, 2026, 12:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69fef888a7988190837f3f4b8d340e04 completed May 9, 2026, 9:04 a.m.
Created at: April 10, 2026, 2:53 a.m.