Triple

T732451
Position Surface form Disambiguated ID Type / Status
Subject Operation Olympic E14860 entity
Predicate target P860 FINISHED
Object Kyushu E13149 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: Kyushu | Statement: [Operation Olympic, target, Kyushu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kyushu
Context triple: [Operation Olympic, target, Kyushu]
  • A. Kyushu chosen
    Kyushu is the southwesternmost of Japan’s main islands, known for its active volcanoes, hot springs, and historic cities such as Fukuoka and Nagasaki.
  • B. Shikoku
    Shikoku is the smallest of Japan’s four main islands, known for its mountainous landscapes, traditional rural culture, and the famous 88-temple Buddhist pilgrimage route.
  • C. Honshu
    Honshu is the largest and most populous island of Japan, home to major cities such as Tokyo, Osaka, and Kyoto.
  • D. Hokkaido
    Hokkaido is Japan’s northernmost main island, known for its cold climate, vast natural landscapes, and popular ski resorts.
  • E. Kagoshima Prefecture
    Kagoshima Prefecture is a southern Japanese prefecture on Kyushu known for its active volcano Sakurajima, hot springs, and subtropical island landscapes.
  • 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_69a4934d9930819099eed80096b0597d completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a5d5a6b48190a2c81bfc3c6faa25 completed March 1, 2026, 8:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69af2b3a21248190aca7710ae6ad6478 completed March 9, 2026, 8:19 p.m.
Created at: March 1, 2026, 7:37 p.m.