Triple

T3480524
Position Surface form Disambiguated ID Type / Status
Subject Fuquay-Varina E73477 entity
Predicate locatedNear P294 FINISHED
Object Garner E5231 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: Garner | Statement: [Fuquay-Varina, locatedNear, Garner]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Garner
Context triple: [Fuquay-Varina, locatedNear, Garner]
  • A. Garner chosen
    Garner is a surname most notably associated with John Nance Garner, the 32nd vice president of the United States under Franklin D. Roosevelt.
  • B. Coker
    Coker is a residential and commercial neighborhood located within the Surulere area of Lagos, Nigeria.
  • C. Garner Ted Armstrong
    Garner Ted Armstrong was an American televangelist and religious broadcaster known for his influential role in the Worldwide Church of God and later for founding the Church of God International.
  • D. Eldridge
    Eldridge is an English-language surname of Old English origin, borne by various notable individuals across fields such as politics, the arts, and sports.
  • E. Grier
    Grier is the surname of Pam Grier, an influential American actress renowned for her groundbreaking roles in 1970s blaxploitation films.
  • 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_69ad85b3c9b08190857cae74c7f36da9 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adbb7461708190898002fbd1191f34 completed March 8, 2026, 6:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69b3681b30b48190918d2821a8383229 completed March 13, 2026, 1:27 a.m.
Created at: March 8, 2026, 3:17 p.m.