Triple

T19801491
Position Surface form Disambiguated ID Type / Status
Subject Miura District E475686 entity
Predicate hasJapaneseName P9882 FINISHED
Object 三浦郡
三浦郡 is a rural district located in Kanagawa Prefecture, Japan, known for encompassing parts of the Miura Peninsula with coastal landscapes and small communities.
E1395102 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: 三浦郡 | Statement: [Miura District, hasJapaneseName, 三浦郡]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: 三浦郡
Context triple: [Miura District, hasJapaneseName, 三浦郡]
  • A. 都筑郡
    都筑郡(Tsuzuki District)は、かつて神奈川県に属していた日本の郡で、現在の横浜市都筑区などを含む地域として知られていた行政区画である。
  • B. 本郷区
    本郷区 was a former ward of Tokyo, historically known as an educational and cultural center that included areas around the University of Tokyo before being incorporated into modern Bunkyō Ward.
  • C. 磯子区
    磯子区 is one of the 18 administrative wards of Yokohama in Kanagawa Prefecture, Japan, known as a coastal industrial and residential area facing Tokyo Bay.
  • D. 木津川市
    木津川市は、京都府南部に位置し、奈良県に隣接する住宅都市・歴史観光地として発展している市です。
  • E. 川越市
    川越市 is a historic city in Saitama Prefecture, Japan, famed for its well-preserved Edo-period streetscapes and traditional warehouse-style buildings that have earned it the nickname "Little Edo."
  • 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: 三浦郡
Triple: [Miura District, hasJapaneseName, 三浦郡]
Generated description
三浦郡 is a rural district located in Kanagawa Prefecture, Japan, known for encompassing parts of the Miura Peninsula with coastal landscapes and small communities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: 三浦郡
Target entity description: 三浦郡 is a rural district located in Kanagawa Prefecture, Japan, known for encompassing parts of the Miura Peninsula with coastal landscapes and small communities.
  • A. 都筑郡
    都筑郡(Tsuzuki District)は、かつて神奈川県に属していた日本の郡で、現在の横浜市都筑区などを含む地域として知られていた行政区画である。
  • B. 本郷区
    本郷区 was a former ward of Tokyo, historically known as an educational and cultural center that included areas around the University of Tokyo before being incorporated into modern Bunkyō Ward.
  • C. 磯子区
    磯子区 is one of the 18 administrative wards of Yokohama in Kanagawa Prefecture, Japan, known as a coastal industrial and residential area facing Tokyo Bay.
  • D. 木津川市
    木津川市は、京都府南部に位置し、奈良県に隣接する住宅都市・歴史観光地として発展している市です。
  • E. 川越市
    川越市 is a historic city in Saitama Prefecture, Japan, famed for its well-preserved Edo-period streetscapes and traditional warehouse-style buildings that have earned it the nickname "Little Edo."
  • 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_69d8e51bc4208190a1c57d8c5d1b15e4 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e653cc995c81908e4ca85b0639d541 completed April 20, 2026, 4:26 p.m.
NED1 Entity disambiguation (via context triple) batch_6a07c5088834819086f4b8ac1e33401b completed May 16, 2026, 1:14 a.m.
NEDg Description generation batch_6a07c5f5a2808190bab75011262f86e6 completed May 16, 2026, 1:18 a.m.
NED2 Entity disambiguation (via description) batch_6a07c6745828819085ea6103a723380a completed May 16, 2026, 1:20 a.m.
Created at: April 10, 2026, 1:49 p.m.