Triple
T2810660
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kashiwara |
E54159
|
entity |
| Predicate | hasNameInJapanese |
P28734
|
FINISHED |
| Object |
柏原市
柏原市は、大阪府南東部に位置し、歴史ある寺社やぶどう栽培などで知られる中規模の都市です。
|
E302198
|
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: 柏原市 | Statement: [Kashiwara, hasNameInJapanese, 柏原市]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 柏原市 Context triple: [Kashiwara, hasNameInJapanese, 柏原市]
-
A.
高槻市
高槻市は、大阪府北部に位置し、京都と大阪の中間にあるベッドタウン兼商工業都市です。
-
B.
枚方市
枚方市は、大阪府北東部に位置し、淀川沿いに広がる住宅都市兼商業都市として発展している市です。
-
C.
八幡市
八幡市は、京都府南部に位置し、石清水八幡宮などで知られる歴史と自然に恵まれた都市です。
-
D.
伊丹市
伊丹市は、兵庫県南東部に位置し、大阪国際空港（伊丹空港）を擁する都市です。
-
E.
和光市
和光市 is a suburban city in southern Saitama Prefecture, Japan, known for hosting research institutes and serving as a residential area for commuters to Tokyo.
- 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: 柏原市 Triple: [Kashiwara, hasNameInJapanese, 柏原市]
Generated description
柏原市は、大阪府南東部に位置し、歴史ある寺社やぶどう栽培などで知られる中規模の都市です。
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 柏原市 Target entity description: 柏原市は、大阪府南東部に位置し、歴史ある寺社やぶどう栽培などで知られる中規模の都市です。
-
A.
高槻市
高槻市は、大阪府北部に位置し、京都と大阪の中間にあるベッドタウン兼商工業都市です。
-
B.
枚方市
枚方市は、大阪府北東部に位置し、淀川沿いに広がる住宅都市兼商業都市として発展している市です。
-
C.
八幡市
八幡市は、京都府南部に位置し、石清水八幡宮などで知られる歴史と自然に恵まれた都市です。
-
D.
伊丹市
伊丹市は、兵庫県南東部に位置し、大阪国際空港（伊丹空港）を擁する都市です。
-
E.
和光市
和光市 is a suburban city in southern Saitama Prefecture, Japan, known for hosting research institutes and serving as a residential area for commuters to Tokyo.
- 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_69ab49dcee188190b5c6eca9ae9e3469 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abde335b38819090c70d5e2ca14d79 |
completed | March 7, 2026, 8:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afce9a76388190a5dce756de2eb59f |
completed | March 10, 2026, 7:56 a.m. |
| NEDg | Description generation | batch_69afd0b179ac8190a260003b9a457180 |
completed | March 10, 2026, 8:05 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afd10b07748190935cce94cd9b2a13 |
completed | March 10, 2026, 8:06 a.m. |
Created at: March 6, 2026, 9:59 p.m.