Triple

T1365613
Position Surface form Disambiguated ID Type / Status
Subject Kobe Airport E29195 entity
Predicate locatedIn P40 FINISHED
Object Kobe E3089 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 | Statement: [Kobe Airport, locatedIn, Kobe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kobe
Context triple: [Kobe Airport, locatedIn, Kobe]
  • A. Kobe chosen
    Kobe is a major port city in Japan’s Kansai region, known for its scenic harbor setting, cosmopolitan atmosphere, and famous Kobe beef.
  • B. Hotto Motto Field Kobe
    Hotto Motto Field Kobe is a baseball stadium in Kobe, Japan, known for hosting Nippon Professional Baseball games and serving as a secondary home venue for the Orix Buffaloes.
  • C. Kobe Chinatown
    Kobe Chinatown is a compact, bustling Chinese district in central Kobe, Japan, known for its vibrant streets, food stalls, and cultural festivals.
  • D. Henderson
    Henderson is a major city in the Las Vegas metropolitan area known for its rapid growth, residential communities, and proximity to the Las Vegas Strip.
  • E. Daikanyama
    Daikanyama is a trendy, upscale neighborhood in Tokyo known for its stylish boutiques, cafes, and relaxed, residential atmosphere.
  • 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_69a498d77abc8190913bf57e5f51d2c4 completed March 1, 2026, 7:51 p.m.
NER Named-entity recognition batch_69a4c2d1d15481909d58b6fd8aa2e585 completed March 1, 2026, 10:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad36fb2c348190860129f84ca59cc3 completed March 8, 2026, 8:44 a.m.
Created at: March 1, 2026, 7:57 p.m.