Triple

T10011768
Position Surface form Disambiguated ID Type / Status
Subject Beibu Wan E199389 entity
Predicate hasCoastlineIn P212 FINISHED
Object Hai Phong E28530 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: Hai Phong | Statement: [Beibu Wan, hasCoastlineIn, Hai Phong]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hai Phong
Context triple: [Beibu Wan, hasCoastlineIn, Hai Phong]
  • A. Hai Phong chosen
    Hai Phong is a major port city in northern Vietnam known for its industrial economy and coastal location.
  • B. Hanoi
    Hanoi is the historic and modern capital of Vietnam, known for its centuries-old architecture, rich cultural heritage, and vibrant street life.
  • C. 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.
  • D. Hai Duong
    Hai Duong is a provincial city in northern Vietnam known as an important industrial and transportation hub between Hanoi and Hai Phong.
  • E. Vinh
    Vinh is a major city in north-central Vietnam, serving as the economic and cultural center of Nghệ An Province.
  • 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_69ca8315a1a08190ab310f25620f362b completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cdcd3b68888190b8a325b52d57c5b8 completed April 2, 2026, 1:58 a.m.
NED1 Entity disambiguation (via context triple) batch_69dbb6bb34548190aac9d2af05477750 completed April 12, 2026, 3:14 p.m.
Created at: March 30, 2026, 8:52 p.m.