Triple

T12000256
Position Surface form Disambiguated ID Type / Status
Subject Tsu E285638 entity
Predicate hasJapaneseName P9882 FINISHED
Object 津市
津市 is a coastal city in Mie Prefecture, Japan, known as the prefectural capital and an important regional administrative and transportation hub.
 E959006 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: 津市 | Statement: [Tsu, hasJapaneseName, 津市]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: 津市
Context triple: [Tsu, hasJapaneseName, 津市]
  • A. 浏阳
    浏阳是中国湖南省东部的一座县级市，以烟花爆竹产业和红色革命历史而闻名。
  • B. Yiyang
    Yiyang is a prefecture-level city in south-central China known for its location along the Zi River and its role as an important regional center in Hunan Province.
  • C. Loudi
    Loudi is a prefecture-level city in south-central China known for its role as an industrial and transportation hub within Hunan Province.
  • D. Changde
    Changde is a city in northwestern Hunan Province, China, historically significant as a major battleground during the Second Sino-Japanese War.
  • E. Xiangxiang City
    Xiangxiang City is a county-level city in Hunan Province, China, administered by the prefecture-level city of Xiangtan and known for its historical and cultural heritage.
  • 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: 津市
Triple: [Tsu, hasJapaneseName, 津市]
Generated description
津市 is a coastal city in Mie Prefecture, Japan, known as the prefectural capital and an important regional administrative and transportation hub.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: 津市
Target entity description: 津市 is a coastal city in Mie Prefecture, Japan, known as the prefectural capital and an important regional administrative and transportation hub.
  • A. 浏阳
    浏阳是中国湖南省东部的一座县级市，以烟花爆竹产业和红色革命历史而闻名。
  • B. Yiyang
    Yiyang is a prefecture-level city in south-central China known for its location along the Zi River and its role as an important regional center in Hunan Province.
  • C. Loudi
    Loudi is a prefecture-level city in south-central China known for its role as an industrial and transportation hub within Hunan Province.
  • D. Changde
    Changde is a city in northwestern Hunan Province, China, historically significant as a major battleground during the Second Sino-Japanese War.
  • E. Xiangxiang City
    Xiangxiang City is a county-level city in Hunan Province, China, administered by the prefecture-level city of Xiangtan and known for its historical and cultural heritage.
  • 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_69d6ab44a77c8190a652f4b27164e4ef completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d903c26d7881909b67a31d04882eb5 completed April 10, 2026, 2:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69f4729eb4a081909d93b3fc74509d86 completed May 1, 2026, 9:30 a.m.
NEDg Description generation batch_69f47b7e4a40819085680c48eed5418a completed May 1, 2026, 10:07 a.m.
NED2 Entity disambiguation (via description) batch_69f47df40a8c8190bd7350ba27f57214 completed May 1, 2026, 10:18 a.m.
Created at: April 8, 2026, 9:46 p.m.