Triple
T12592550
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 世田谷区 |
E300641
|
entity |
| Predicate | traversedByRiver |
P165
|
FINISHED |
| Object |
呑川
呑川は東京都世田谷区などを流れ東京湾へ注ぐ都市河川で、かつては農業用水としても利用された中小河川です。
|
E993880
|
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: 呑川 | Statement: [世田谷区, traversedByRiver, 呑川]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 呑川 Context triple: [世田谷区, traversedByRiver, 呑川]
-
A.
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。
-
B.
天神川
天神川は兵庫県伊丹市を流れる中小河川で、市街地の治水や景観に寄与している川です。
-
C.
庄川
庄川は日本の中部地方を流れ、富山県の庄川峡などの景勝地や水力発電で知られる河川である。
-
D.
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.
-
E.
桂川
桂川は、京都市内を流れ嵐山の景観で知られる日本の代表的な河川の一つです。
- 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: 呑川 Triple: [世田谷区, traversedByRiver, 呑川]
Generated description
呑川は東京都世田谷区などを流れ東京湾へ注ぐ都市河川で、かつては農業用水としても利用された中小河川です。
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 呑川 Target entity description: 呑川は東京都世田谷区などを流れ東京湾へ注ぐ都市河川で、かつては農業用水としても利用された中小河川です。
-
A.
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。
-
B.
天神川
天神川は兵庫県伊丹市を流れる中小河川で、市街地の治水や景観に寄与している川です。
-
C.
庄川
庄川は日本の中部地方を流れ、富山県の庄川峡などの景勝地や水力発電で知られる河川である。
-
D.
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.
-
E.
桂川
桂川は、京都市内を流れ嵐山の景観で知られる日本の代表的な河川の一つです。
- 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_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.