Triple

T12228693
Position Surface form Disambiguated ID Type / Status
Subject Janet Lam E291418 entity
Predicate countryOfCitizenship P2 FINISHED
Object Hong Kong E8492 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: Hong Kong | Statement: [Janet Lam, countryOfCitizenship, Hong Kong]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hong Kong
Context triple: [Janet Lam, countryOfCitizenship, Hong Kong]
  • A. Hong Kong, China chosen
    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.
  • B. Kowloon
    Kowloon is a densely populated urban area of Hong Kong known for its vibrant street life, markets, and skyline facing Victoria Harbour.
  • C. Macau
    Macau is a Special Administrative Region of China known for its blend of Portuguese and Chinese cultures and its major casino and tourism industry.
  • D. Macau
    Macau is a coastal municipality in the Brazilian state of Rio Grande do Norte, known for its salt production and fishing activities.
  • E. Wan Chai, Hong Kong
    Wan Chai, Hong Kong is a bustling commercial and entertainment district on Hong Kong Island known for its mix of office towers, shopping, nightlife, and historic sites.
  • 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_69d6ab668acc8190963ba424049d6aee completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d91ca34fe88190900c8791c70948b7 completed April 10, 2026, 3:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69f66849ba888190a50c5a5fcdb935e0 completed May 2, 2026, 9:10 p.m.
Created at: April 8, 2026, 9:51 p.m.