Triple

T4306744
Position Surface form Disambiguated ID Type / Status
Subject Cần Thơ E99971 entity
Predicate sharesStatusWith P8274 FINISHED
Object Đà Nẵng E28418 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: Đà Nẵng | Statement: [Cần Thơ, sharesStatusWith, Đà Nẵng]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Đà Nẵng
Context triple: [Cần Thơ, sharesStatusWith, Đà Nẵng]
  • A. Da Nang chosen
    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.
  • B. Nha Trang
    Nha Trang is a coastal resort city in Vietnam renowned for its sandy beaches, scuba diving, and vibrant tourism industry.
  • C. Huế
    Huế is a historic city in central Vietnam that served as the imperial capital of the Nguyễn Dynasty and is renowned for its ancient citadel, royal tombs, and rich cultural heritage.
  • D. Vung Tau
    Vung Tau is a coastal city in southern Vietnam known as a major seaside resort and important maritime and oil industry hub.
  • E. Hai Phong
    Hai Phong is a major port city in northern Vietnam known for its industrial economy and coastal location.
  • 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_69b345528ebc8190b5abc7e95094792d completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b350bb78cc8190a850aca47d8711cf completed March 12, 2026, 11:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5d07aede08190bbabc6a7b5a7dfb6 completed March 14, 2026, 9:17 p.m.
Created at: March 12, 2026, 11:09 p.m.