Triple

T370222
Position Surface form Disambiguated ID Type / Status
Subject Nguyen Duy Trinh E8251 entity
Predicate residence P75 FINISHED
Object Hanoi E6204 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: Hanoi | Statement: [Nguyen Duy Trinh, residence, Hanoi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hanoi
Context triple: [Nguyen Duy Trinh, residence, Hanoi]
  • A. Hanoi chosen
    Hanoi is the historic and modern capital of Vietnam, known for its centuries-old architecture, rich cultural heritage, and vibrant street life.
  • B. Saigon
    Saigon, now officially known as Ho Chi Minh City, is Vietnam’s largest city and a historic economic and cultural hub in the south of the country.
  • C. Hai Phong
    Hai Phong is a major port city in northern Vietnam known for its industrial economy and coastal location.
  • D. Thu Duc City
    Thu Duc City is an eastern municipal city within Ho Chi Minh City (Saigon), Vietnam, formed by merging several urban districts into a major innovation and technology hub.
  • E. Da Nang
    Da Nang is a major coastal city in central Vietnam known for its sandy beaches, modern infrastructure, and proximity to historic sites like Hoi An and the Marble Mountains.
  • 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_69a2e7f2ec648190b42bc7db424f8109 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ebff472881909fad81d597425ea6 completed Feb. 28, 2026, 1:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3f0a78c748190ae5e64919f1d6501 completed March 1, 2026, 7:54 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.