Triple

T21943048
Position Surface form Disambiguated ID Type / Status
Subject Sze Yup region E541868 entity
Predicate hasAlternativeName P39 FINISHED
Object Four Counties NE NERFINISHED

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: Four Counties | Statement: [Sze Yup region, hasAlternativeName, Four Counties]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Four Counties
Context triple: [Sze Yup region, hasAlternativeName, Four Counties]
  • A. Four Counties chosen
    Four Counties is a historical region in Guangdong, China, known as the ancestral homeland of many overseas Chinese communities, particularly in North America.
  • B. Rutland
    Rutland is an unincorporated community located in Bibb County, Georgia, United States.
  • C. Rutland
    Rutland is a small town in Worcester County, Massachusetts, known for its rural character and location near the geographic center of the state.
  • D. Rutland
    Rutland is a small city in central Vermont known historically as a marble quarrying center and as a regional hub for commerce and outdoor recreation.
  • E. Rutland
    Rutland is a small historic county in the East Midlands of England, known for its rural character and Rutland Water reservoir.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0c47e2e5c81909a7f74ce3de50911 completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f1242515ec8190b015bf8c7b13be85 completed April 28, 2026, 9:18 p.m.
Created at: April 16, 2026, 7:56 p.m.