Triple

T4452269
Position Surface form Disambiguated ID Type / Status
Subject Frank Money E97640 entity
Predicate returnsTo P45021 FINISHED
Object Lotus, Georgia E439700 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: Lotus, Georgia | Statement: [Frank Money, returnsTo, Lotus, Georgia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lotus, Georgia
Context triple: [Frank Money, returnsTo, Lotus, Georgia]
  • A. Lotus, Georgia chosen
    Lotus, Georgia is the small, segregated rural Southern town that serves as the haunting hometown setting in Toni Morrison’s novel "Home."
  • B. Grovania, Georgia
    Grovania, Georgia is an unincorporated community located in Houston County in the central part of the state.
  • C. Valdosta, Georgia
    Valdosta, Georgia is a small city in southern Georgia known as a regional hub for education, retail, and sports, particularly high school football.
  • D. Lithonia, Georgia
    Lithonia, Georgia is a small city in the eastern Atlanta metropolitan area known historically for its granite quarries and African American heritage.
  • E. Lebanon, Georgia
    Lebanon, Georgia is a small unincorporated community located in Cherokee County in the U.S. state of Georgia.
  • 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_69b3454777808190b78aa9047ba1f018 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b355f3731c81909cc5a782b12ddd38 completed March 13, 2026, 12:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69b6281cef58819095182f8c89fe6e59 completed March 15, 2026, 3:31 a.m.
Created at: March 12, 2026, 11:33 p.m.