Triple
T13361782
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beihai Park |
E318835
|
entity |
| Predicate | ChineseName |
P744
|
FINISHED |
| Object |
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。
|
E1036760
|
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: 北海公园 | Statement: [Beihai Park, ChineseName, 北海公园]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 北海公园 Context triple: [Beihai Park, ChineseName, 北海公园]
-
A.
颐和园
颐和园是位于北京市西北部、以宏伟的皇家园林建筑和昆明湖、万寿山自然景观著称的世界文化遗产。
-
B.
天安门
天安门是位于北京市中心、作为中国象征性地标和重要政治历史事件发生地的著名城门与广场名称。
-
C.
Zhongshan Park (Beijing)
Zhongshan Park (Beijing) is a historic public park adjacent to the Forbidden City, known for its classical Chinese gardens, cultural relics, and memorials.
-
D.
圜丘坛
圜丘坛是位于北京天坛内、明清两代皇帝举行祭天大典的重要露天祭坛建筑。
-
E.
太庙
太庙是北京故宫东侧一座明清皇家宗庙建筑群，现为对公众开放的历史文化景区。
- 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: 北海公园 Triple: [Beihai Park, ChineseName, 北海公园]
Generated description
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。
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 北海公园 Target entity description: 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。
-
A.
颐和园
颐和园是位于北京市西北部、以宏伟的皇家园林建筑和昆明湖、万寿山自然景观著称的世界文化遗产。
-
B.
天安门
天安门是位于北京市中心、作为中国象征性地标和重要政治历史事件发生地的著名城门与广场名称。
-
C.
Zhongshan Park (Beijing)
Zhongshan Park (Beijing) is a historic public park adjacent to the Forbidden City, known for its classical Chinese gardens, cultural relics, and memorials.
-
D.
圜丘坛
圜丘坛是位于北京天坛内、明清两代皇帝举行祭天大典的重要露天祭坛建筑。
-
E.
太庙
太庙是北京故宫东侧一座明清皇家宗庙建筑群，现为对公众开放的历史文化景区。
- 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_69d806b7bbac8190b85278c87fa7aff3 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69da628affd081909f1790d333f0eef4 |
completed | April 11, 2026, 3:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7267ab580819091577c24dd952c99 |
completed | May 3, 2026, 10:42 a.m. |
| NEDg | Description generation | batch_69f7277a73248190aa59a997d719cab8 |
completed | May 3, 2026, 10:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7281e150081909a92201ceb30b8d6 |
completed | May 3, 2026, 10:49 a.m. |
Created at: April 9, 2026, 9:32 p.m.