Triple
T14634046
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bell Tower of Beijing |
E343553
|
entity |
| Predicate | hasChineseName |
P4878
|
FINISHED |
| Object |
北京钟楼
北京钟楼是位于北京市中心、与鼓楼相对而立的一座历史悠久的古代报时建筑和著名文化地标。
|
E1110681
|
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: 北京钟楼 | Statement: [Bell Tower of Beijing, hasChineseName, 北京钟楼]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 北京钟楼 Context triple: [Bell Tower of Beijing, hasChineseName, 北京钟楼]
-
A.
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。
-
B.
北京皇城
北京皇城是明清两代北京城中围绕皇宫设置的内城区域,汇集重要宫殿、坛庙和皇家建筑群的核心防御与礼制空间。
-
C.
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。
-
D.
故宫
故宫是位于北京市中心、以明清皇宫建筑群著称的中国古代皇家宫殿与世界文化遗产。
-
E.
崇文门
崇文门是北京明清时期内城东南部的一座重要城门遗址及地名,如今也是地铁与商业区的交通枢纽。
- 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: 北京钟楼 Triple: [Bell Tower of Beijing, hasChineseName, 北京钟楼]
Generated description
北京钟楼是位于北京市中心、与鼓楼相对而立的一座历史悠久的古代报时建筑和著名文化地标。
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 北京钟楼 Target entity description: 北京钟楼是位于北京市中心、与鼓楼相对而立的一座历史悠久的古代报时建筑和著名文化地标。
-
A.
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。
-
B.
北京皇城
北京皇城是明清两代北京城中围绕皇宫设置的内城区域,汇集重要宫殿、坛庙和皇家建筑群的核心防御与礼制空间。
-
C.
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。
-
D.
故宫
故宫是位于北京市中心、以明清皇宫建筑群著称的中国古代皇家宫殿与世界文化遗产。
-
E.
崇文门
崇文门是北京明清时期内城东南部的一座重要城门遗址及地名,如今也是地铁与商业区的交通枢纽。
- 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_69d822dffc3c8190aa173b90761bffda |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb4aa7cb48190b008bd6b0e162c89 |
completed | April 14, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fda933937881909f3cf59fba878dfd |
completed | May 8, 2026, 9:13 a.m. |
| NEDg | Description generation | batch_69fdb3efb4fc8190bca7469d89a66a85 |
completed | May 8, 2026, 9:59 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fdb48b5ca08190be61da2fdb7dbce4 |
completed | May 8, 2026, 10:01 a.m. |
Created at: April 10, 2026, 1:26 a.m.