Triple

T1603679
Position Surface form Disambiguated ID Type / Status
Subject Hakka E34449 entity
Predicate spokenIn P2266 FINISHED
Object Macau E7092 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: Macau | Statement: [Hakka, spokenIn, Macau]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Macau
Context triple: [Hakka, spokenIn, Macau]
  • A. Macau chosen
    Macau is a Special Administrative Region of China known for its blend of Portuguese and Chinese cultures and its major casino and tourism industry.
  • B. Macau
    Macau is a coastal municipality in the Brazilian state of Rio Grande do Norte, known for its salt production and fishing activities.
  • C. Magong
    Magong is the main urban center and largest city of Taiwan’s Penghu (Pescadores) archipelago, serving as its political, economic, and transportation hub.
  • D. Hong Kong, China
    Hong Kong, China is a major global financial and trading hub and a Special Administrative Region of China located on the southern coast of the country.
  • E. Zhuhai
    Zhuhai is a coastal city in Guangdong Province, China, known for its proximity to Macau, its role in the Pearl River Delta economic zone, and its reputation as a popular tourist destination.
  • 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_69a885fea6a481909fe83ba6441f1774 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a9094f96ec819090286c21b3dfddd5 completed March 5, 2026, 4:40 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad608dfc688190a81a502c810f67fb completed March 8, 2026, 11:42 a.m.
Created at: March 4, 2026, 7:28 p.m.