Triple
T20768234
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kamikawa |
E511156
|
entity |
| Predicate | hasJapaneseName |
P9882
|
FINISHED |
| Object |
上川町
上川町は、北海道上川総合振興局に属し、大雪山国立公園への玄関口として知られる自然豊かな町です。
|
E1450027
|
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: 上川町 | Statement: [Kamikawa, hasJapaneseName, 上川町]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 上川町 Context triple: [Kamikawa, hasJapaneseName, 上川町]
-
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.
瑞穂町
瑞穂町は、日本の東京都西多摩郡に位置する住宅地と自然が混在する町です。
-
D.
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."
-
E.
寝屋川市
寝屋川市は、大阪府北河内地域に位置し、住宅地と商業地が広がる中核的な都市です。
- 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: 上川町 Triple: [Kamikawa, hasJapaneseName, 上川町]
Generated description
上川町は、北海道上川総合振興局に属し、大雪山国立公園への玄関口として知られる自然豊かな町です。
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 上川町 Target entity description: 上川町は、北海道上川総合振興局に属し、大雪山国立公園への玄関口として知られる自然豊かな町です。
-
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.
瑞穂町
瑞穂町は、日本の東京都西多摩郡に位置する住宅地と自然が混在する町です。
-
D.
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."
-
E.
寝屋川市
寝屋川市は、大阪府北河内地域に位置し、住宅地と商業地が広がる中核的な都市です。
- 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_69e0b4ca01148190ac018e57e0cab46f |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c24fa1b08190b09ab8fcb87b5c01 |
completed | April 21, 2026, 12:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08ef8d27dc8190a6ae623f4bbe452e |
completed | May 16, 2026, 10:28 p.m. |
| NEDg | Description generation | batch_6a08f28a96488190bf80089105facd0b |
completed | May 16, 2026, 10:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08f316e7988190920ec8eeb56e76a2 |
completed | May 16, 2026, 10:43 p.m. |
Created at: April 16, 2026, 12:36 p.m.