Triple

T15479953
Position Surface form Disambiguated ID Type / Status
Subject Kamogawa E376888 entity
Predicate romanization P2508 FINISHED
Object Kamo-gawa NE NERFINISHED

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: Kamo-gawa | Statement: [Kamogawa, romanization, Kamo-gawa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kamo-gawa
Context triple: [Kamogawa, romanization, Kamo-gawa]
  • A. Kamogawa chosen
    Kamogawa is a prominent river running through Kyoto, Japan, known for its scenic banks, cultural significance, and popular walking paths.
  • B. Kamogawa
    Kamogawa is a coastal city in Chiba Prefecture, Japan, known for its beaches, fishing industry, and the popular Kamogawa Sea World aquarium.
  • C. Okehazama
    Okehazama is a historical area in present-day Nagoya, Japan, best known as the site of Oda Nobunaga’s decisive victory over Imagawa Yoshimoto in 1560.
  • D. Nagaokakyo
    Nagaokakyo is a suburban city in Japan known for its bamboo groves, historical temples, and convenient location between Kyoto and Osaka.
  • E. Kizugawa
    Kizugawa is a city in southern Kyoto Prefecture, Japan, known for its mix of historical sites, residential areas, and growing industrial and research facilities.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d85cd21dcc81908646251b1c26ea00 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e03f8a77a081909f12f13660452f4a completed April 16, 2026, 1:46 a.m.
Created at: April 10, 2026, 3:34 a.m.