Triple

T11231109
Position Surface form Disambiguated ID Type / Status
Subject IBM Pavilion E265821 entity
Predicate location P40 FINISHED
Object Tsukuba E526806 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: Tsukuba | Statement: [IBM Pavilion, location, Tsukuba]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tsukuba
Context triple: [IBM Pavilion, location, 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 (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_69d6aac656d48190b275efaa7d6074ee completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e9026e1c81909456ac946bbba972 completed April 9, 2026, 5:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00077201c08190a0c3bb259856d5c9 completed May 10, 2026, 4:20 a.m.
Created at: April 8, 2026, 9:30 p.m.