Triple
T19604319
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 柏崎市 |
E470563
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object |
小千谷市
小千谷市は、新潟県中越地方に位置し、信濃川沿いの風景や小千谷縮などの伝統織物で知られる市です。
|
E1384620
|
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: 小千谷市 | Statement: [柏崎市, borderedBy, 小千谷市]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 小千谷市 Context triple: [柏崎市, borderedBy, 小千谷市]
-
A.
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。
-
B.
高千穂町
高千穂町は、宮崎県北西部に位置し、神話の里として知られる峡谷や高千穂神社などの観光名所で有名な町です。
-
C.
宍粟市
宍粟市は、兵庫県西部の中国山地に位置し、豊かな森林資源と自然環境を特徴とする市です。
-
D.
米原市
米原市は、滋賀県北東部に位置し、東海道新幹線や在来線が交差する交通の要衝として知られる市です。
-
E.
秦野市
秦野市 is a city in Kanagawa Prefecture, Japan, known for its natural scenery, hiking spots, and agricultural products such as peanuts and tobacco.
- 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: 小千谷市 Triple: [柏崎市, borderedBy, 小千谷市]
Generated description
小千谷市は、新潟県中越地方に位置し、信濃川沿いの風景や小千谷縮などの伝統織物で知られる市です。
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 小千谷市 Target entity description: 小千谷市は、新潟県中越地方に位置し、信濃川沿いの風景や小千谷縮などの伝統織物で知られる市です。
-
A.
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。
-
B.
高千穂町
高千穂町は、宮崎県北西部に位置し、神話の里として知られる峡谷や高千穂神社などの観光名所で有名な町です。
-
C.
宍粟市
宍粟市は、兵庫県西部の中国山地に位置し、豊かな森林資源と自然環境を特徴とする市です。
-
D.
米原市
米原市は、滋賀県北東部に位置し、東海道新幹線や在来線が交差する交通の要衝として知られる市です。
-
E.
秦野市
秦野市 is a city in Kanagawa Prefecture, Japan, known for its natural scenery, hiking spots, and agricultural products such as peanuts and tobacco.
- 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_69d8e510024481908415c0d616fa6186 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e64081af6c8190868b73b07c874cd5 |
completed | April 20, 2026, 3:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a075f1fe3e8819088c2e1b5145e1f92 |
completed | May 15, 2026, 6 p.m. |
| NEDg | Description generation | batch_6a075fcda360819093ef08e89680c3ae |
completed | May 15, 2026, 6:02 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a07604b2fe4819090be363844abf2ec |
completed | May 15, 2026, 6:04 p.m. |
Created at: April 10, 2026, 1:43 p.m.