Triple

T8397100
Position Surface form Disambiguated ID Type / Status
Subject Kita, Tokyo E198080 entity
Predicate hasJapaneseName P9882 FINISHED
Object 北区
北区 is one of Tokyo’s 23 special wards, located in the northern part of the city and known for its mix of residential neighborhoods, parks, and major railway hubs.
 E730539 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: 北区 | Statement: [Kita, Tokyo, hasJapaneseName, 北区]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: 北区
Context triple: [Kita, Tokyo, hasJapaneseName, 北区]
  • A. 北区
    北区是韩国蔚山市的一个行政区，以其工业设施与城市居住功能并存而闻名。
  • B. 东区
    东区是韩国蔚山广域市的一个行政区，以工业设施与港口经济为主。
  • C. 中央区
    中央区 is one of Tokyo’s 23 special wards, known as a major commercial and financial center that includes districts such as Ginza and Nihonbashi.
  • D. Jiefangbei area
    The Jiefangbei area is a bustling commercial and entertainment district in central Chongqing, China, known for its landmark Liberation Monument, dense high-rises, and vibrant shopping streets.
  • E. Beibei District
    Beibei District is an urban district of Chongqing, China, known for its scenic landscapes, hot springs, and educational institutions.
  • 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: 北区
Triple: [Kita, Tokyo, hasJapaneseName, 北区]
Generated description
北区 is one of Tokyo’s 23 special wards, located in the northern part of the city and known for its mix of residential neighborhoods, parks, and major railway hubs.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: 北区
Target entity description: 北区 is one of Tokyo’s 23 special wards, located in the northern part of the city and known for its mix of residential neighborhoods, parks, and major railway hubs.
  • A. 北区
    北区是韩国蔚山市的一个行政区，以其工业设施与城市居住功能并存而闻名。
  • B. 东区
    东区是韩国蔚山广域市的一个行政区，以工业设施与港口经济为主。
  • C. 中央区
    中央区 is one of Tokyo’s 23 special wards, known as a major commercial and financial center that includes districts such as Ginza and Nihonbashi.
  • D. Jiefangbei area
    The Jiefangbei area is a bustling commercial and entertainment district in central Chongqing, China, known for its landmark Liberation Monument, dense high-rises, and vibrant shopping streets.
  • E. Beibei District
    Beibei District is an urban district of Chongqing, China, known for its scenic landscapes, hot springs, and educational institutions.
  • 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_69ca82f816bc8190ab321c07d72208c1 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb818893348190a6ea2ff6a2e3e491 completed March 31, 2026, 8:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69cde867d21c8190b066a6c88273ec5a completed April 2, 2026, 3:54 a.m.
NEDg Description generation batch_69cdebfd60188190a1681344e2bf1e9e completed April 2, 2026, 4:09 a.m.
NED2 Entity disambiguation (via description) batch_69cded2fa42c8190bfbfc79caf38bf8e completed April 2, 2026, 4:14 a.m.
Created at: March 30, 2026, 6:04 p.m.