Triple

T7825059
Position Surface form Disambiguated ID Type / Status
Subject Kejia E181224 entity
Predicate hasDialect P4251 FINISHED
Object Wuhua Hakka E185931 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: Wuhua Hakka | Statement: [Kejia, hasDialect, Wuhua Hakka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wuhua Hakka
Context triple: [Kejia, hasDialect, Wuhua Hakka]
  • A. Wuhua Hakka chosen
    Wuhua Hakka is a regional variety of the Hakka Chinese language spoken primarily in Wuhua County, Guangdong, known for its distinctive phonological and lexical features.
  • B. Heyuan Hakka
    Heyuan Hakka is a regional variety of the Hakka Chinese language spoken primarily in and around Heyuan in Guangdong Province, China.
  • C. Ganxian Hakka
    Ganxian Hakka is a regional variety of the Hakka Chinese language spoken primarily in and around Gan County in Jiangxi Province, China.
  • D. Yuebei Hakka
    Yuebei Hakka is a regional variety of the Hakka Chinese language spoken primarily in the northern part of Guangdong province, China.
  • E. Jiaoling Hakka
    Jiaoling Hakka is a regional variety of the Hakka Chinese language spoken primarily in Jiaoling County, Guangdong, known for its distinctive phonological and lexical features within the Hakka dialect group.
  • 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_69ca8282ccec819083c48efb72d21cf9 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cafa0c1f5c8190b16db20daad159a1 completed March 30, 2026, 10:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69ccbd8698a88190b5f9b4d232504f04 completed April 1, 2026, 6:39 a.m.
Created at: March 30, 2026, 4:42 p.m.