Triple
T13903041
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lingyin Temple |
E334276
|
entity |
| Predicate | nativeName |
P15
|
FINISHED |
| Object |
灵隐寺
灵隐寺是位于中国浙江杭州西湖附近、始建于东晋时期的著名古刹和佛教禅宗道场。
|
E1068115
|
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: 灵隐寺 | Statement: [Lingyin Temple, nativeName, 灵隐寺]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 灵隐寺 Context triple: [Lingyin Temple, nativeName, 灵隐寺]
-
A.
佛香阁
佛香阁是位于北京颐和园万寿山上的标志性古建筑，也是清代皇家园林中最著名的佛教建筑之一。
-
B.
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。
-
C.
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。
-
D.
法门寺
法门寺 is a famous Buddhist temple in Fufeng County, Shaanxi, China, renowned as a historic pilgrimage site that houses revered relics of the Buddha.
-
E.
三井寺
三井寺は、滋賀県大津市にある天台寺門宗の総本山で、琵琶湖近くに位置する歴史ある古刹です。
- 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: 灵隐寺 Triple: [Lingyin Temple, nativeName, 灵隐寺]
Generated description
灵隐寺是位于中国浙江杭州西湖附近、始建于东晋时期的著名古刹和佛教禅宗道场。
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 灵隐寺 Target entity description: 灵隐寺是位于中国浙江杭州西湖附近、始建于东晋时期的著名古刹和佛教禅宗道场。
-
A.
佛香阁
佛香阁是位于北京颐和园万寿山上的标志性古建筑，也是清代皇家园林中最著名的佛教建筑之一。
-
B.
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。
-
C.
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。
-
D.
法门寺
法门寺 is a famous Buddhist temple in Fufeng County, Shaanxi, China, renowned as a historic pilgrimage site that houses revered relics of the Buddha.
-
E.
三井寺
三井寺は、滋賀県大津市にある天台寺門宗の総本山で、琵琶湖近くに位置する歴史ある古刹です。
- 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_69d81c5eaa9c819083b1ff8689179565 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de25db1e308190aaed6a21e443cc44 |
completed | April 14, 2026, 11:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7c722e72081909090b2d64000ebd9 |
completed | May 3, 2026, 10:07 p.m. |
| NEDg | Description generation | batch_69f7c83a3e04819097b6e0b5a3161b9a |
completed | May 3, 2026, 10:12 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7c976e96c8190b59f46d9b758e8e2 |
completed | May 3, 2026, 10:17 p.m. |
Created at: April 9, 2026, 10:16 p.m.