Triple

T6400450
Position Surface form Disambiguated ID Type / Status
Subject Taitung County E144047 entity
Predicate languageUsed P238 FINISHED
Object Hakka E34449 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: Hakka | Statement: [Taitung County, languageUsed, Hakka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hakka
Context triple: [Taitung County, languageUsed, Hakka]
  • A. Hakka chosen
    Hakka is a Sinitic language spoken primarily by the Hakka people across southern China and various overseas Chinese communities.
  • B. Hokkien
    Hokkien is a Southern Min Chinese language variety widely spoken in Taiwan, Southeast Asia, and parts of southern China, known for its rich tonal system and distinct vocabulary from Mandarin.
  • C. Chōmin
    Chōmin was the pen name of Nakae Chōmin, a prominent Japanese political theorist, journalist, and early advocate of liberal democracy during the Meiji era.
  • D. Guanggu
    Guanggu is a major high-tech development zone in Wuhan, China, known as an innovation hub for the optics and electronics industries.
  • E. Hui
    The Hui are a predominantly Muslim ethnic group in China known for their integration of Islamic faith with Han Chinese language and cultural practices.
  • 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_69c008dc56fc81908d43ffcc11d73bdd completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c0689ae99881909ba427769fd317ae completed March 22, 2026, 10:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6389fe9608190adc68f3ad5b0471a completed March 27, 2026, 7:58 a.m.
Created at: March 22, 2026, 4:35 p.m.