Triple

T1514199
Position Surface form Disambiguated ID Type / Status
Subject Penghu Islands E32081 entity
Predicate hasAirport P105 FINISHED
Object Penghu Airport E162851 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: Penghu Airport | Statement: [Penghu Islands, hasAirport, Penghu Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Penghu Airport
Context triple: [Penghu Islands, hasAirport, Penghu Airport]
  • A. Penghu Airport chosen
    Penghu Airport is a regional airport in Taiwan serving the Penghu (Pescadores) archipelago with domestic flights and acting as its main air transportation hub.
  • B. Taipei Songshan Airport
    Taipei Songshan Airport is a domestic and regional international airport located near the center of Taipei, Taiwan, serving as a key hub for short-haul flights.
  • C. Kaohsiung International Airport
    Kaohsiung International Airport is a major airport in southern Taiwan that serves as an important regional gateway for both domestic and international flights.
  • D. Taiwan Taoyuan International Airport
    Taiwan Taoyuan International Airport is the main international gateway to northern Taiwan and the primary airport serving the Taipei metropolitan area.
  • E. Yao Airport
    Yao Airport is a regional airport in Yao, Osaka Prefecture, Japan, primarily serving general aviation, flight training, and emergency services rather than major commercial airline traffic.
  • 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_69a885e8caf88190a5fbb6159ce87786 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a907d901ac8190be55ed4bac609d1d completed March 5, 2026, 4:34 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad23409fd481909834aaf0dc4641f6 completed March 8, 2026, 7:20 a.m.
Created at: March 4, 2026, 7:26 p.m.