Triple
T14249221
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 赤坂御用地 |
E353212
|
entity |
| Predicate | 周辺地域 |
P17964
|
FINISHED |
| Object |
赤坂
赤坂は、東京都港区に位置し、官公庁や高級ホテル、飲食店が集まるビジネス・商業エリアとして知られる街です。
|
E1090342
|
NE FINISHED |
How this triple was built (5 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: 赤坂 | Statement: [赤坂御用地, 周辺地域, 赤坂]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 赤坂 Context triple: [赤坂御用地, 周辺地域, 赤坂]
-
A.
赤坂御用地
赤坂御用地は、東京都港区赤坂に位置する日本の皇室関連施設が集まる広大な御用地で、皇族方の御所や宮邸が置かれているエリアである。
-
B.
大手町
大手町 is a major business district in central Tokyo known for its concentration of corporate headquarters, financial institutions, and proximity to the Imperial Palace.
-
C.
高田馬場
高田馬場 is a bustling neighborhood in Tokyo’s Shinjuku ward known for its major train station, student population, and numerous eateries and entertainment spots.
-
D.
下北沢
下北沢は東京都世田谷区に位置する、古着店やライブハウス、個性的なカフェが集まる若者文化とサブカルチャーの発信地として知られる街です。
-
E.
北沢
北沢は、東京都世田谷区に位置する住宅地と商業地が混在した地域で、下北沢などの繁華なエリアを含む街区である。
- 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: 赤坂 Triple: [赤坂御用地, 周辺地域, 赤坂]
Generated description
赤坂は、東京都港区に位置し、官公庁や高級ホテル、飲食店が集まるビジネス・商業エリアとして知られる街です。
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 赤坂 Target entity description: 赤坂は、東京都港区に位置し、官公庁や高級ホテル、飲食店が集まるビジネス・商業エリアとして知られる街です。
-
A.
赤坂御用地
赤坂御用地は、東京都港区赤坂に位置する日本の皇室関連施設が集まる広大な御用地で、皇族方の御所や宮邸が置かれているエリアである。
-
B.
大手町
大手町 is a major business district in central Tokyo known for its concentration of corporate headquarters, financial institutions, and proximity to the Imperial Palace.
-
C.
高田馬場
高田馬場 is a bustling neighborhood in Tokyo’s Shinjuku ward known for its major train station, student population, and numerous eateries and entertainment spots.
-
D.
下北沢
下北沢は東京都世田谷区に位置する、古着店やライブハウス、個性的なカフェが集まる若者文化とサブカルチャーの発信地として知られる街です。
-
E.
北沢
北沢は、東京都世田谷区に位置する住宅地と商業地が混在した地域で、下北沢などの繁華なエリアを含む街区である。
- F. None of above. chosen
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 周辺地域 Context triple: [赤坂御用地, 周辺地域, 赤坂]
-
A.
nearbySettlementRegion
Indicates that a settlement is located close to or within the surrounding area of a specified region.
-
B.
regionOfCity
Indicates that a specified area or district is a constituent part or subdivision of a particular city.
-
C.
neighboringRegion
chosen
Indicates that two regions share a common boundary or are directly adjacent to each other geographically.
-
D.
nearbyRegionCharacterizedBy
Indicates that a region located nearby another entity is defined or distinguished by a particular characteristic, feature, or condition.
-
E.
hasNearbyPrefecture
Indicates that one administrative region has another prefecture located geographically close to it.
- F. None of above.
Provenance (6 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_69d8278c43e08190824146f4632b89a5 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de6295ef9081909cfb0c1283bca21a |
completed | April 14, 2026, 3:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd325815d48190b070866f41986847 |
completed | May 8, 2026, 12:46 a.m. |
| NEDg | Description generation | batch_69fd367fd9788190bd25f057d1f0942c |
completed | May 8, 2026, 1:04 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd37c1f4c0819085f5c577e673e9df |
completed | May 8, 2026, 1:09 a.m. |
| PD | Predicate disambiguation | batch_69de05c09b7881908acbca18bd7d997c |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 10, 2026, 1:08 a.m.