Triple

T176338
Position Surface form Disambiguated ID Type / Status
Subject Portuguese language E3583 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: [Portuguese language, spokenIn, Macau]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Macau
Context triple: [Portuguese language, 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. 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.
  • C. Shanghai
    Shanghai is a major global financial hub and China’s largest city, known for its modern skyline, historic waterfront, and role as a center of international business and trade.
  • D. Taipei, Taiwan
    Taipei, Taiwan is the capital and largest city of Taiwan, known as a major political, economic, and cultural center in East Asia.
  • E. Guangzhou
    Guangzhou is a major port city in southern China and the capital of Guangdong Province, known as a key commercial and manufacturing hub in the Pearl River Delta.
  • 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_69a25374990081909766d30c79a18e0e completed Feb. 28, 2026, 2:31 a.m.
NER Named-entity recognition batch_69a258e497788190aeb61d981efb4d1d completed Feb. 28, 2026, 2:54 a.m.
NED1 Entity disambiguation (via context triple) batch_69a3161ab0548190b4c0ed74a79cea46 completed Feb. 28, 2026, 4:21 p.m.
Created at: Feb. 28, 2026, 2:39 a.m.