Triple
T3122872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ponto-chō |
E65226
|
entity |
| Predicate | hasNameInLanguage |
P15
|
FINISHED |
| Object |
先斗町
先斗町(ぽんとちょう)は、京都市中心部の鴨川西岸に位置し、細い路地に茶屋や飲食店が並ぶ花街として知られる歴史的な歓楽街です。
|
E328625
|
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: 先斗町 | Statement: [Ponto-chō, hasNameInLanguage, 先斗町]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 先斗町 Context triple: [Ponto-chō, hasNameInLanguage, 先斗町]
-
A.
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。
-
B.
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。
-
C.
交野市
交野市は、大阪府北河内地域に位置する自然豊かな住宅都市で、星田妙見宮や天野川などで知られる市です。
-
D.
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。
-
E.
Kitano-cho
Kitano-cho is a historic district in Kobe, Japan, known for its preserved Western-style residences built by foreign merchants in the late 19th and early 20th centuries.
- 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: 先斗町 Triple: [Ponto-chō, hasNameInLanguage, 先斗町]
Generated description
先斗町(ぽんとちょう)は、京都市中心部の鴨川西岸に位置し、細い路地に茶屋や飲食店が並ぶ花街として知られる歴史的な歓楽街です。
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 先斗町 Target entity description: 先斗町(ぽんとちょう)は、京都市中心部の鴨川西岸に位置し、細い路地に茶屋や飲食店が並ぶ花街として知られる歴史的な歓楽街です。
-
A.
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。
-
B.
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。
-
C.
交野市
交野市は、大阪府北河内地域に位置する自然豊かな住宅都市で、星田妙見宮や天野川などで知られる市です。
-
D.
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。
-
E.
Kitano-cho
Kitano-cho is a historic district in Kobe, Japan, known for its preserved Western-style residences built by foreign merchants in the late 19th and early 20th centuries.
- 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_69ad8580c72481909672d37acf647893 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada52c105c8190b8128e66d9b9e8a0 |
completed | March 8, 2026, 4:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b20f72c2048190ab2aa40a109f5976 |
completed | March 12, 2026, 12:57 a.m. |
| NEDg | Description generation | batch_69b21083db7081908f8bc4240fc2b08b |
completed | March 12, 2026, 1:01 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b210fa8a7c8190ae4527161aa3af54 |
completed | March 12, 2026, 1:03 a.m. |
Created at: March 8, 2026, 3:04 p.m.