Triple

T12675464
Position Surface form Disambiguated ID Type / Status
Subject Port Liner E302798 entity
Predicate servesFacility P26183 FINISHED
Object Kobe Airport E29195 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: Kobe Airport | Statement: [Port Liner, servesFacility, Kobe Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kobe Airport
Context triple: [Port Liner, servesFacility, Kobe Airport]
  • A. Kobe Airport chosen
    Kobe Airport is a regional airport located on an artificial island off the coast of Kobe, Japan, primarily serving domestic flights.
  • B. Kansai International Airport
    Kansai International Airport is a major international airport in Japan built on an artificial island in Osaka Bay, serving as a key gateway to the Kansai region.
  • C. Osaka International Airport
    Osaka International Airport is a major Japanese airport serving the Osaka metropolitan area, primarily handling domestic flights and known locally as Itami Airport.
  • D. Clow International Airport
    Clow International Airport is a public general aviation airport located in Bolingbrook, Illinois, primarily serving private and recreational pilots.
  • E. Haneda Airport
    Haneda Airport is one of Tokyo’s primary international airports and one of Japan’s busiest air travel hubs.
  • 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_69d7bdee64a08190801c6d470aefd723 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d961b0d9c88190a05d6cbcb7a1642d completed April 10, 2026, 8:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6b8c37eb08190a4c15cb50f84c341 completed May 3, 2026, 2:53 a.m.
Created at: April 9, 2026, 5:20 p.m.