Triple
T14213973
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Puning Temple |
E352300
|
entity |
| Predicate | ChineseName |
P744
|
FINISHED |
| Object |
普宁寺
普宁寺是位于河北省承德市避暑山庄外的一座清代皇家寺庙，以其宏伟的大佛殿和世界上最大的木雕千手观音像而闻名。
|
E1086078
|
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: 普宁寺 | Statement: [Puning Temple, ChineseName, 普宁寺]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 普宁寺 Context triple: [Puning Temple, ChineseName, 普宁寺]
-
A.
仁和寺
仁和寺（Ninna-ji） is a historic Buddhist temple in Kyoto, Japan, renowned as a former imperial monastery and a UNESCO World Heritage Site noted for its classical architecture and gardens.
-
B.
白马寺
白马寺是位于中国河南省洛阳市、被誉为中国第一古刹的著名佛教寺院。
-
C.
归元禅寺
归元禅寺是一座位于湖北省武汉市汉阳区、以古朴建筑和众多佛像而闻名的历史悠久佛教寺院。
-
D.
五台山
五台山是位于中国山西省、以文殊菩萨道场著称的佛教名山和世界文化遗产。
-
E.
北海公园
北海公园是位于北京市中心、以皇家园林景观和历史文化遗迹著称的大型古典园林公园。
- 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: 普宁寺 Triple: [Puning Temple, ChineseName, 普宁寺]
Generated description
普宁寺是位于河北省承德市避暑山庄外的一座清代皇家寺庙，以其宏伟的大佛殿和世界上最大的木雕千手观音像而闻名。
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 普宁寺 Target entity description: 普宁寺是位于河北省承德市避暑山庄外的一座清代皇家寺庙，以其宏伟的大佛殿和世界上最大的木雕千手观音像而闻名。
-
A.
仁和寺
仁和寺（Ninna-ji） is a historic Buddhist temple in Kyoto, Japan, renowned as a former imperial monastery and a UNESCO World Heritage Site noted for its classical architecture and gardens.
-
B.
白马寺
白马寺是位于中国河南省洛阳市、被誉为中国第一古刹的著名佛教寺院。
-
C.
归元禅寺
归元禅寺是一座位于湖北省武汉市汉阳区、以古朴建筑和众多佛像而闻名的历史悠久佛教寺院。
-
D.
五台山
五台山是位于中国山西省、以文殊菩萨道场著称的佛教名山和世界文化遗产。
-
E.
北海公园
北海公园是位于北京市中心、以皇家园林景观和历史文化遗迹著称的大型古典园林公园。
- 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_69d8278a06e481908b5d6af0a8afe737 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de620f07bc81909212dcd1c91b5f95 |
completed | April 14, 2026, 3:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd1959f3d481909c15730bbd6f4748 |
completed | May 7, 2026, 10:59 p.m. |
| NEDg | Description generation | batch_69fd1a88fd948190b5d78a4ca4acdb94 |
completed | May 7, 2026, 11:04 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd1b2ed7748190b3f787f1b64c8831 |
completed | May 7, 2026, 11:07 p.m. |
Created at: April 10, 2026, 1:06 a.m.