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.