Triple

T17144631
Position Surface form Disambiguated ID Type / Status
Subject Shikoku Mura E416058 entity
Predicate name P16 FINISHED
Object Shikoku Mura E416058 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: Shikoku Mura | Statement: [Shikoku Mura, name, Shikoku Mura]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Shikoku Mura
Context triple: [Shikoku Mura, name, Shikoku Mura]
  • A. Shikoku Mura chosen
    Shikoku Mura is an open-air museum in Takamatsu that preserves and exhibits traditional buildings and structures from across Japan’s Shikoku region.
  • B. Asuka-mura
    Asuka-mura is a historic village in Nara Prefecture, Japan, renowned as one of the cradles of early Japanese civilization and culture.
  • C. Fukusaki
    Fukusaki is a town in Hyōgo Prefecture, Japan, known for its rural setting and association with folklorist Kunio Yanagita.
  • D. Kudamatsu
    Kudamatsu is a coastal city in western Japan known for its industrial facilities and location along the Seto Inland Sea in Yamaguchi Prefecture.
  • E. Shikaoi
    Shikaoi is a rural town in Hokkaido, Japan, known for its natural scenery, agriculture, and access to outdoor activities such as hiking and hot springs.
  • 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_69d886d15af4819092f92f8a129763e6 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3f2d8ca8c81909bba0cd6d60a4776 completed April 18, 2026, 9:08 p.m.
NED1 Entity disambiguation (via context triple) batch_6a01415718c88190834fedae7b01ac69 completed May 11, 2026, 2:39 a.m.
Created at: April 10, 2026, 5:36 a.m.