Triple

T12015401
Position Surface form Disambiguated ID Type / Status
Subject Ontario Teachers’ Pension Plan E286009 entity
Predicate hasOfficeIn P1268 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: [Ontario Teachers’ Pension Plan, hasOfficeIn, Hong Kong]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hong Kong
Context triple: [Ontario Teachers’ Pension Plan, hasOfficeIn, 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. Pokfulam, Hong Kong
    Pokfulam, Hong Kong is a residential and institutional district on the western side of Hong Kong Island, known for housing the main campus of the University of Hong Kong and a mix of historic villages, public housing, and green hillsides.
  • 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_69d6ab45a368819084fce08bf0dc3705 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d903d9b17881908894be80d7c1b64e completed April 10, 2026, 2:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69f5f63ac68c819090a0361a16e8452d completed May 2, 2026, 1:03 p.m.
Created at: April 8, 2026, 9:47 p.m.