Triple

T574381
Position Surface form Disambiguated ID Type / Status
Subject Shikoku E13729 entity
Predicate largestCity P235 FINISHED
Object Matsuyama E202029 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: Matsuyama | Statement: [Shikoku, largestCity, Matsuyama]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Matsuyama
Context triple: [Shikoku, largestCity, Matsuyama]
  • A. Matsuyama chosen
    Matsuyama is a major city on Japan’s Shikoku Island, known for its historic Dōgo Onsen hot spring and Matsuyama Castle.
  • B. Daikanyama
    Daikanyama is a trendy, upscale neighborhood in Tokyo known for its stylish boutiques, cafes, and relaxed, residential atmosphere.
  • C. Takarazuka
    Takarazuka is a Japanese city in Hyōgo Prefecture best known for the all-female Takarazuka Revue theater troupe and its popular hot spring resorts.
  • D. Niigata
    Niigata is a major coastal city in north-central Japan known for its important seaport on the Sea of Japan, rice production, and sake brewing.
  • E. Okayama
    Okayama is a major city in western Japan known for its historic Okayama Castle, the celebrated Korakuen Garden, and its role as a regional transportation and cultural hub.
  • 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_69a4933fa4d88190a7949cc83c08c5c1 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49b4c23548190a3b883239c7c78c8 completed March 1, 2026, 8:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69aeb39a387c8190b5c1f4876532e376 completed March 9, 2026, 11:48 a.m.
Created at: March 1, 2026, 7:33 p.m.