Triple

T10939740
Position Surface form Disambiguated ID Type / Status
Subject Kagawa Prefecture E258435 entity
Predicate hasCity P316 FINISHED
Object Ayagawa
Ayagawa is a small town in Kagawa Prefecture on Japan’s Shikoku island, known for its rural landscapes and traditional agricultural character.
 E992234 NE FINISHED

How this triple was built (4 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: Ayagawa | Statement: [Kagawa Prefecture, hasCity, Ayagawa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ayagawa
Context triple: [Kagawa Prefecture, hasCity, Ayagawa]
  • A. Takinogawa
    Takinogawa is a residential district in Kita Ward, Tokyo, known for its quiet neighborhoods and convenient urban access.
  • B. Kisogawa
    Kisogawa is the Japanese name for the Kiso River, a major river in central Honshu known for its scenic valleys and historical importance.
  • C. Ogawa
    Ogawa is a town in Saitama Prefecture, Japan, known for its traditional Japanese paper (washi) production and its role as a local transport hub.
  • D. 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.
  • E. Kamogawa
    Kamogawa is a coastal city in Chiba Prefecture, Japan, known for its beaches, fishing industry, and the popular Kamogawa Sea World aquarium.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Ayagawa
Triple: [Kagawa Prefecture, hasCity, Ayagawa]
Generated description
Ayagawa is a small town in Kagawa Prefecture on Japan’s Shikoku island, known for its rural landscapes and traditional agricultural character.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ayagawa
Target entity description: Ayagawa is a small town in Kagawa Prefecture on Japan’s Shikoku island, known for its rural landscapes and traditional agricultural character.
  • A. Takinogawa
    Takinogawa is a residential district in Kita Ward, Tokyo, known for its quiet neighborhoods and convenient urban access.
  • B. Kisogawa
    Kisogawa is the Japanese name for the Kiso River, a major river in central Honshu known for its scenic valleys and historical importance.
  • C. Ogawa
    Ogawa is a town in Saitama Prefecture, Japan, known for its traditional Japanese paper (washi) production and its role as a local transport hub.
  • D. 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.
  • E. Kamogawa
    Kamogawa is a coastal city in Chiba Prefecture, Japan, known for its beaches, fishing industry, and the popular Kamogawa Sea World aquarium.
  • F. None of above. chosen

Provenance (5 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_69d6aa8769b4819082bfe5e61b9017f0 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d770c1389881909341170984211810 completed April 9, 2026, 9:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69f65e9136bc8190b35685376da7007e completed May 2, 2026, 8:29 p.m.
NEDg Description generation batch_69f660bc541c8190a4d1d7a4cc959ecf completed May 2, 2026, 8:38 p.m.
NED2 Entity disambiguation (via description) batch_69f6617997188190bfce14c54619af7f completed May 2, 2026, 8:41 p.m.
Created at: April 8, 2026, 9:23 p.m.