Triple
T12592549
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 世田谷区 |
E300641
|
entity |
| Predicate | traversedByRiver |
P165
|
FINISHED |
| Object |
野川
野川は東京都多摩地域から世田谷区などを流れ、多摩川へと注ぐ都市近郊の中小河川です。
|
E993879
|
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: 野川 | Statement: [世田谷区, traversedByRiver, 野川]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 野川 Context triple: [世田谷区, traversedByRiver, 野川]
-
A.
野洲川
野洲川 is a river in Shiga Prefecture, Japan, that flows into Lake Biwa and is known for its role in local agriculture and flood control.
-
B.
天野川
天野川は大阪府交野市を流れる、七夕伝説や星にまつわるロマンチックな物語で知られる川です。
-
C.
白川
白川 is a picturesque canal in Kyoto, Japan, renowned for its traditional townscape and cherry blossom-lined banks.
-
D.
宇治川
宇治川は京都府南部を流れ、歴史的な合戦や宇治茶の産地として知られる日本の河川である。
-
E.
富士川
富士川は、山梨県と静岡県を流れ駿河湾に注ぐ、日本有数の急流として知られる大河川である。
- 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: 野川 Triple: [世田谷区, traversedByRiver, 野川]
Generated description
野川は東京都多摩地域から世田谷区などを流れ、多摩川へと注ぐ都市近郊の中小河川です。
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 野川 Target entity description: 野川は東京都多摩地域から世田谷区などを流れ、多摩川へと注ぐ都市近郊の中小河川です。
-
A.
野洲川
野洲川 is a river in Shiga Prefecture, Japan, that flows into Lake Biwa and is known for its role in local agriculture and flood control.
-
B.
天野川
天野川は大阪府交野市を流れる、七夕伝説や星にまつわるロマンチックな物語で知られる川です。
-
C.
白川
白川 is a picturesque canal in Kyoto, Japan, renowned for its traditional townscape and cherry blossom-lined banks.
-
D.
宇治川
宇治川は京都府南部を流れ、歴史的な合戦や宇治茶の産地として知られる日本の河川である。
-
E.
富士川
富士川は、山梨県と静岡県を流れ駿河湾に注ぐ、日本有数の急流として知られる大河川である。
- 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_69d7bde87b648190bcd0266e9efde098 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d954cc6d3c81908fbb22601c46f3f7 |
completed | April 10, 2026, 7:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65ec2dac88190bf31bb00f93feb30 |
completed | May 2, 2026, 8:29 p.m. |
| NEDg | Description generation | batch_69f66308087c81908ab5b5795f255e37 |
completed | May 2, 2026, 8:48 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f663fe2fac8190bb70c8f1b919d657 |
completed | May 2, 2026, 8:52 p.m. |
Created at: April 9, 2026, 5:07 p.m.