Triple

T599754
Position Surface form Disambiguated ID Type / Status
Subject Portuguese Empire E11466 entity
Predicate territory P2160 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 Empire, territory, Macau]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Macau
Context triple: [Portuguese Empire, territory, 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. 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.
  • D. Kowloon
    Kowloon is a densely populated urban area of Hong Kong known for its vibrant street life, markets, and skyline facing Victoria Harbour.
  • E. Pearl River Delta
    The Pearl River Delta is a highly urbanized and economically vital megaregion in southern China, encompassing major cities such as Guangzhou, Shenzhen, and Hong Kong.
  • 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_69a4932779b881908688590d59c71900 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49d78c0f08190b83ad89062ccb0b9 completed March 1, 2026, 8:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69a51f37f8748190bff705fd2bbc489c completed March 2, 2026, 5:25 a.m.
Created at: March 1, 2026, 7:35 p.m.