Triple

T15495833
Position Surface form Disambiguated ID Type / Status
Subject Terminal 2 E378813 entity
Predicate locatedIn P40 FINISHED
Object Dayuan District E445775 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: Dayuan District | Statement: [Terminal 2, locatedIn, Dayuan District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dayuan District
Context triple: [Terminal 2, locatedIn, Dayuan District]
  • A. Dayuan District chosen
    Dayuan District is a coastal district of Taoyuan City in northwestern Taiwan best known for hosting Taiwan Taoyuan International Airport, the country’s main international gateway.
  • B. Yantan District
    Yantan District is an urban administrative district of Zigong, a prefecture-level city in Sichuan Province, China.
  • C. Daoli District
    Daoli District is a central urban district of Harbin, China, known for its historic architecture, commercial streets, and role as a cultural and administrative hub of the city.
  • D. Lishi District
    Lishi District is an urban administrative district in Shanxi Province, China, serving as the political and economic center of Lüliang City.
  • E. Qidu District
    Qidu District is an administrative district of Keelung City in northern Taiwan, known for its residential areas and transportation links to greater Taipei.
  • 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_69d85cd53a7c819080f5b9042c4c199e completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e03faecd60819091eeaa56c9c8f67d completed April 16, 2026, 1:47 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0084a6d6308190ad57a51b380171a2 completed May 10, 2026, 1:14 p.m.
Created at: April 10, 2026, 3:52 a.m.