Triple

T17166637
Position Surface form Disambiguated ID Type / Status
Subject Norma Jean E416627 entity
Predicate origin P410 FINISHED
Object Douglasville, Georgia E411999 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: Douglasville, Georgia | Statement: [Norma Jean, origin, Douglasville, Georgia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Douglasville, Georgia
Context triple: [Norma Jean, origin, Douglasville, Georgia]
  • A. Douglasville, Georgia chosen
    Douglasville, Georgia is a suburban city in the Atlanta metropolitan area known for its historic downtown and role as a regional commercial and residential hub.
  • B. Douglas, Georgia
    Douglas, Georgia is a small city in south-central Georgia that serves as the county seat of Coffee County and a regional hub for agriculture and industry.
  • C. Danville, Georgia
    Danville, Georgia is a small rural town in central Georgia known for its quiet community and location along major transportation routes between Macon and Dublin.
  • D. Snellville, Georgia
    Snellville, Georgia is a suburban city in Gwinnett County known for its residential communities, local parks, and proximity to Atlanta.
  • E. Dunwoody, Georgia
    Dunwoody, Georgia is a suburban city in the Atlanta metropolitan area known for its residential neighborhoods, shopping centers, and business districts.
  • 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_69d886d279c081909f8ff1f743ddeb69 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3f915622c8190a6dff0baf0288a62 completed April 18, 2026, 9:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a01483d754c819089607cfc87d08d42 completed May 11, 2026, 3:08 a.m.
Created at: April 10, 2026, 5:37 a.m.