Triple

T3340886
Position Surface form Disambiguated ID Type / Status
Subject Gakushuin Primary School E70256 entity
Predicate region P40 FINISHED
Object Kanto E51702 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: Kanto | Statement: [Gakushuin Primary School, region, Kanto]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kanto
Context triple: [Gakushuin Primary School, region, Kanto]
  • A. Kanto chosen
    Kanto is a major geographical and metropolitan region of eastern Japan that includes Tokyo and several surrounding prefectures.
  • B. Geita Region
    Geita Region is an administrative region in northwestern Tanzania, known for its significant gold mining activities and proximity to Lake Victoria.
  • C. Kantōgun
    Kantōgun was the Imperial Japanese Army's Kwantung Army, a powerful and influential military force stationed in Manchuria that played a central role in Japan's expansionist policies before and during World War II.
  • D. Tokai
    Tokai is a suburb in Cape Town, South Africa, known for its residential areas, green spaces, and proximity to the Constantiaberg mountains.
  • E. Volta Region
    The Volta Region is an eastern administrative region of Ghana known for its diverse Ewe culture, lush landscapes, and attractions such as Lake Volta and Wli Waterfalls.
  • 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_69ad85a405e48190b6e68de7cf9f319e completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb1c0ae44819091c851569eaf4565 completed March 8, 2026, 5:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69b31a92555c81909a4769b6ecea5721 completed March 12, 2026, 7:57 p.m.
Created at: March 8, 2026, 3:12 p.m.