Triple

T2566964
Position Surface form Disambiguated ID Type / Status
Subject Katsura River E57373 entity
Predicate hasJapaneseName P9882 FINISHED
Object 桂川
桂川は、京都市内を流れ嵐山の景観で知られる日本の代表的な河川の一つです。
 E279172 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: 桂川 | Statement: [Katsura River, hasJapaneseName, 桂川]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: 桂川
Context triple: [Katsura River, hasJapaneseName, 桂川]
  • A. 山本
    山本 is a common Japanese surname borne by many notable figures across fields such as politics, sports, and the arts.
  • B. 栗林忠道
    栗林忠道は、第二次世界大戦中に硫黄島守備隊を指揮し、徹底した防御戦術で知られる日本陸軍の将軍です。
  • C. 本間雅晴
    本間雅晴は、第二次世界大戦期にフィリピン侵攻作戦を指揮し、戦後に戦争犯罪で裁かれた日本陸軍の軍人である。
  • D. Yoshida
    Yoshida is a common Japanese surname borne by numerous notable figures in politics, arts, sports, and entertainment.
  • E. 古賀峯一
    古賀峯一は、太平洋戦争中に連合艦隊司令長官を務めた日本海軍の軍人である。
  • 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: 桂川
Triple: [Katsura River, hasJapaneseName, 桂川]
Generated description
桂川は、京都市内を流れ嵐山の景観で知られる日本の代表的な河川の一つです。
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: 桂川
Target entity description: 桂川は、京都市内を流れ嵐山の景観で知られる日本の代表的な河川の一つです。
  • A. 山本
    山本 is a common Japanese surname borne by many notable figures across fields such as politics, sports, and the arts.
  • B. 栗林忠道
    栗林忠道は、第二次世界大戦中に硫黄島守備隊を指揮し、徹底した防御戦術で知られる日本陸軍の将軍です。
  • C. 本間雅晴
    本間雅晴は、第二次世界大戦期にフィリピン侵攻作戦を指揮し、戦後に戦争犯罪で裁かれた日本陸軍の軍人である。
  • D. Yoshida
    Yoshida is a common Japanese surname borne by numerous notable figures in politics, arts, sports, and entertainment.
  • E. 古賀峯一
    古賀峯一は、太平洋戦争中に連合艦隊司令長官を務めた日本海軍の軍人である。
  • 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_69ab4a4ef9008190a0e6d4422b9418b7 completed March 6, 2026, 9:42 p.m.
NER Named-entity recognition batch_69abd3602ed08190aad0f9c7ac577eb0 completed March 7, 2026, 7:27 a.m.
NED1 Entity disambiguation (via context triple) batch_69af6565e05081909dc12aa3240de5f2 completed March 10, 2026, 12:27 a.m.
NEDg Description generation batch_69af667c6b008190b3960f29f5e07653 completed March 10, 2026, 12:31 a.m.
NED2 Entity disambiguation (via description) batch_69af6740cd2c8190a76309238340bd22 completed March 10, 2026, 12:35 a.m.
Created at: March 6, 2026, 9:48 p.m.