Triple

T11627079
Position Surface form Disambiguated ID Type / Status
Subject Magnolia Breeland E276298 entity
Predicate isFrom P11553 FINISHED
Object Bluebell, Alabama E352473 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: Bluebell, Alabama | Statement: [Magnolia Breeland, isFrom, Bluebell, Alabama]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bluebell, Alabama
Context triple: [Magnolia Breeland, isFrom, Bluebell, Alabama]
  • A. Bluebell, Alabama chosen
    Bluebell, Alabama is a fictional small Southern town featured as the primary setting in the television series "Hart of Dixie."
  • B. Berry, Alabama
    Berry, Alabama is a small rural town in western Alabama known for its tight-knit community and location within Fayette County.
  • C. Brent, Alabama
    Brent, Alabama is a small city in central Alabama known for its rural character and location within Bibb County.
  • D. Billingsley, Alabama
    Billingsley, Alabama is a small rural town in central Alabama known for its close-knit community and agricultural surroundings.
  • E. McCalla, Alabama
    McCalla, Alabama is an unincorporated community in Jefferson and Tuscaloosa counties known for its suburban character and proximity to Birmingham and historic sites like Tannehill Ironworks.
  • 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_69d6aafa51148190ab84940694c00235 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a1259cd08190a75eeacb5e39b858 completed April 10, 2026, 7:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69f019075f4c81908e0cde830231b229 completed April 28, 2026, 2:18 a.m.
Created at: April 8, 2026, 9:39 p.m.