Triple

T14088644
Position Surface form Disambiguated ID Type / Status
Subject White County, Georgia E339064 entity
Predicate contains P35 FINISHED
Object Helen, Georgia E546456 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: Helen, Georgia | Statement: [White County, Georgia, contains, Helen, Georgia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Helen, Georgia
Context triple: [White County, Georgia, contains, Helen, Georgia]
  • A. Helen, Georgia chosen
    Helen, Georgia is a small Bavarian-themed alpine village and popular tourist destination in the mountains of northeast Georgia.
  • B. Philema, Georgia
    Philema, Georgia is a small unincorporated community located in rural Lee County in the southwestern part of the state.
  • C. McRae-Helena, Georgia
    McRae-Helena, Georgia is a small city in south-central Georgia that serves as the administrative and commercial hub of Telfair County.
  • D. Haralson, Georgia
    Haralson, Georgia is a small unincorporated community located in Coweta County in the west-central part of the state.
  • E. Hulett, Georgia
    Hulett, Georgia is a small unincorporated rural community located in Carroll County in the western part of the state.
  • 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_69d81c687b0c819087fd9ed4198403f8 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de5ee1ce88819091c983286289337e completed April 14, 2026, 3:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcd0a5c9948190805c2e687c8809ff completed May 7, 2026, 5:49 p.m.
Created at: April 9, 2026, 10:21 p.m.