Triple

T15604264
Position Surface form Disambiguated ID Type / Status
Subject Tsukuba Express E375114 entity
Predicate servesCity P82 FINISHED
Object Tsukuba NE NERFINISHED

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: Tsukuba | Statement: [Tsukuba Express, servesCity, Tsukuba]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tsukuba
Context triple: [Tsukuba Express, servesCity, Tsukuba]
  • A. Tsukuba chosen
    Tsukuba is a planned science and technology city in Ibaraki Prefecture, Japan, known for its research institutions and role as the host of the 1985 World Exposition.
  • B. Takasaki
    Takasaki is a city in Japan’s Gunma Prefecture known for its Daruma doll production and as a regional commercial and transportation hub.
  • C. Akishima
    Akishima is a city in western Tokyo, Japan, known as part of the Tama area and characterized by its residential neighborhoods and light industry.
  • D. Maebashi
    Maebashi is the capital city of Gunma Prefecture in Japan, known as a regional administrative and commercial center on the Kantō Plain.
  • E. Ibaraki City
    Ibaraki City is a suburban city in northern Osaka Prefecture, Japan, known as a residential and commercial hub between Osaka and Kyoto.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d85cce25008190b13b52745fbd719b completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04e7d9328819090e93d55881269a5 completed April 16, 2026, 2:50 a.m.
Created at: April 10, 2026, 4:12 a.m.