Triple
T13957183
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yoshii River |
E335694
|
entity |
| Predicate | hasJapaneseName |
P9882
|
FINISHED |
| Object |
吉井川
吉井川は、岡山県などを流れて瀬戸内海に注ぐ、中国地方を代表する一級河川の一つです。
|
E1071612
|
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: 吉井川 | Statement: [Yoshii River, hasJapaneseName, 吉井川]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 吉井川 Context triple: [Yoshii River, hasJapaneseName, 吉井川]
-
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.
玉川
玉川 is a district in Tokyo’s Setagaya Ward known for its riverside location along the Tama River and its mix of residential areas and commercial facilities.
-
C.
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。
-
D.
石川
石川は大阪府東部を流れ、大和川水系に属する中小河川です。
-
E.
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。
- 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: 吉井川 Triple: [Yoshii River, hasJapaneseName, 吉井川]
Generated description
吉井川は、岡山県などを流れて瀬戸内海に注ぐ、中国地方を代表する一級河川の一つです。
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 吉井川 Target entity description: 吉井川は、岡山県などを流れて瀬戸内海に注ぐ、中国地方を代表する一級河川の一つです。
-
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.
玉川
玉川 is a district in Tokyo’s Setagaya Ward known for its riverside location along the Tama River and its mix of residential areas and commercial facilities.
-
C.
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。
-
D.
石川
石川は大阪府東部を流れ、大和川水系に属する中小河川です。
-
E.
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。
- 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_69d81c61f3508190aaf2ca0dc0002c59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2e7a34f08190aa0d88b66154f268 |
completed | April 14, 2026, 12:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fba1d27a0c8190b5d95ea86fc1a420 |
completed | May 6, 2026, 8:17 p.m. |
| NEDg | Description generation | batch_69fba6af4ed881908cb4b79cfa40977c |
completed | May 6, 2026, 8:38 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fba71a91fc8190b24185994673b33b |
completed | May 6, 2026, 8:39 p.m. |
Created at: April 9, 2026, 10:17 p.m.