Triple

T4357630
Position Surface form Disambiguated ID Type / Status
Subject Margaret Chan E98588 entity
Predicate name P16 FINISHED
Object Margaret Chan E98588 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: Margaret Chan | Statement: [Margaret Chan, name, Margaret Chan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Margaret Chan
Context triple: [Margaret Chan, name, Margaret Chan]
  • A. Margaret Chan chosen
    Margaret Chan is a Chinese-Canadian physician and public health administrator best known for serving as Director-General of the World Health Organization from 2006 to 2017.
  • B. Donald Tsang
    Donald Tsang is a Hong Kong politician who served as the second Chief Executive of Hong Kong from 2005 to 2012.
  • C. Chan Yik-shun
    Chan Yik-shun is the birth name of Eason Chan, a highly acclaimed Hong Kong singer and actor regarded as one of the leading figures in contemporary Cantopop.
  • D. Anita Chan
    Anita Chan is a prominent scholar known for her influential research on Chinese labor issues and labor rights.
  • E. Margaret Chung
    Margaret Chung was a pioneering Chinese American physician and surgeon, widely regarded as the first Chinese American woman doctor in the United States and known for her influential role in supporting U.S. military personnel during World War II.
  • 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_69b3454c772081908e20173e379e8ebe completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b351c7fa1881908bdc844a7142eb65 completed March 12, 2026, 11:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5dbb9b9988190adf8a84de3582ab6 completed March 14, 2026, 10:05 p.m.
Created at: March 12, 2026, 11:16 p.m.