Triple

T15845042
Position Surface form Disambiguated ID Type / Status
Subject North Georgia E384192 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: [North Georgia, contains, Helen, Georgia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Helen, Georgia
Context triple: [North 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. Homer, Georgia
    Homer, Georgia is a small historic town in northeastern Georgia that serves as the administrative and cultural center of Banks County.
  • E. Haralson, Georgia
    Haralson, Georgia is a small unincorporated community located in Coweta County in the west-central 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_69d86da422088190aac39e32e6c68429 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e142eb20088190bb45e37ce3291ef2 completed April 16, 2026, 8:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffa1412c9481909808473e14058033 completed May 9, 2026, 9:04 p.m.
Created at: April 10, 2026, 4:50 a.m.