Triple
T22684132
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 稚内市 |
E560862
|
entity |
| Predicate | adjacentTo |
P224
|
FINISHED |
| Object |
幌延町
幌延町は、北海道北部の宗谷管内に位置し、広大な湿原や牧草地が広がる自然豊かな町です。
|
E1549498
|
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: 幌延町 | Statement: [稚内市, adjacentTo, 幌延町]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 幌延町 Context triple: [稚内市, adjacentTo, 幌延町]
-
A.
隼町
隼町は、東京都千代田区に位置し、国立劇場や最高裁判所などの中枢機関が集まる官庁街として知られる地域です。
-
B.
八幡市
八幡市は、京都府南部に位置し、石清水八幡宮などで知られる歴史と自然に恵まれた都市です。
-
C.
永田町
永田町 is a district in Tokyo’s Chiyoda Ward that serves as Japan’s political center, housing key government institutions and major party headquarters.
-
D.
安平町
安平町は、北海道胆振総合振興局に属し、農業や酪農が盛んで自然豊かな環境を持つ町です。
-
E.
多賀城
多賀城は、奈良時代に東北地方の統治と防衛の拠点として築かれた日本の古代城柵・官衙遺跡です。
- 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: 幌延町 Triple: [稚内市, adjacentTo, 幌延町]
Generated description
幌延町は、北海道北部の宗谷管内に位置し、広大な湿原や牧草地が広がる自然豊かな町です。
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 幌延町 Target entity description: 幌延町は、北海道北部の宗谷管内に位置し、広大な湿原や牧草地が広がる自然豊かな町です。
-
A.
隼町
隼町は、東京都千代田区に位置し、国立劇場や最高裁判所などの中枢機関が集まる官庁街として知られる地域です。
-
B.
八幡市
八幡市は、京都府南部に位置し、石清水八幡宮などで知られる歴史と自然に恵まれた都市です。
-
C.
永田町
永田町 is a district in Tokyo’s Chiyoda Ward that serves as Japan’s political center, housing key government institutions and major party headquarters.
-
D.
安平町
安平町は、北海道胆振総合振興局に属し、農業や酪農が盛んで自然豊かな環境を持つ町です。
-
E.
多賀城
多賀城は、奈良時代に東北地方の統治と防衛の拠点として築かれた日本の古代城柵・官衙遺跡です。
- 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_69e2454d71b48190a1f80af9f82b6fcf |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f17862c8a48190912a8ad09dfda795 |
completed | April 29, 2026, 3:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0b73f4db988190a651faf7f454c6c8 |
completed | May 18, 2026, 8:17 p.m. |
| NEDg | Description generation | batch_6a0b7551043c81908323a86af4db77ce |
completed | May 18, 2026, 8:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0b764c2de88190bb1024206607d107 |
completed | May 18, 2026, 8:27 p.m. |
Created at: April 17, 2026, 3:12 p.m.