Triple

T11605585
Position Surface form Disambiguated ID Type / Status
Subject Mercer University School of Law E275249 entity
Predicate city P40 FINISHED
Object Macon E29211 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: Macon | Statement: [Mercer University School of Law, city, Macon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Macon
Context triple: [Mercer University School of Law, city, Macon]
  • A. Macon
    Macon is a surname of English and French origin borne by various notable individuals, including American statesman Nathaniel Macon.
  • B. Macon
    Macon is a small town located in Warren County, North Carolina, known for its rural character and proximity to Lake Gaston.
  • C. Macon metropolitan area chosen
    The Macon metropolitan area is a regional urban and economic hub in central Georgia centered on the city of Macon and its surrounding communities.
  • D. Marietta
    Marietta is a feminine given name, often considered a diminutive or variant of names like Maria or Marita, used in various European and English-speaking cultures.
  • E. Dawsonville
    Dawsonville is a small city in north Georgia known for its gold rush history and strong ties to stock car racing and NASCAR culture.
  • 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_69d6aaf84b548190ac072e4fb89ae18f completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d895502e0081909ee9c3d45d26cd91 completed April 10, 2026, 6:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69f08f811ff481908e2c14902a3361a2 completed April 28, 2026, 10:44 a.m.
Created at: April 8, 2026, 9:38 p.m.