Triple
T23408613
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Empress Dowager Xiaozhenxian |
E560002
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Wanzhen
Wanzhen was the personal name of Empress Dowager Xiaozhenxian, a Qing dynasty imperial consort who became the mother of the Tongzhi Emperor.
|
E1590088
|
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: Wanzhen | Statement: [Empress Dowager Xiaozhenxian, givenName, Wanzhen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wanzhen Context triple: [Empress Dowager Xiaozhenxian, givenName, Wanzhen]
-
A.
Luzhi
Luzhi is an ancient canal town near Suzhou in China, renowned for its well-preserved waterways, stone bridges, and traditional Jiangnan architecture.
-
B.
Zhishun
Zhishun was a short-lived era name used during the Yuan dynasty in China.
-
C.
Xinzhuang
Xinzhuang is a major suburban town and transportation hub in Shanghai, China, known for its busy commercial areas and key metro and rail connections.
-
D.
Zhangwu
Zhangwu was a historical Chinese era name used during the Three Kingdoms period, specifically associated with the Shu Han state.
-
E.
Wenzheng
Wenzheng is the posthumous honorific title granted to the influential Qing dynasty statesman and military leader Zeng Guofan, reflecting his perceived moral integrity and contributions.
- 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: Wanzhen Triple: [Empress Dowager Xiaozhenxian, givenName, Wanzhen]
Generated description
Wanzhen was the personal name of Empress Dowager Xiaozhenxian, a Qing dynasty imperial consort who became the mother of the Tongzhi Emperor.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Wanzhen Target entity description: Wanzhen was the personal name of Empress Dowager Xiaozhenxian, a Qing dynasty imperial consort who became the mother of the Tongzhi Emperor.
-
A.
Luzhi
Luzhi is an ancient canal town near Suzhou in China, renowned for its well-preserved waterways, stone bridges, and traditional Jiangnan architecture.
-
B.
Zhishun
Zhishun was a short-lived era name used during the Yuan dynasty in China.
-
C.
Xinzhuang
Xinzhuang is a major suburban town and transportation hub in Shanghai, China, known for its busy commercial areas and key metro and rail connections.
-
D.
Zhangwu
Zhangwu was a historical Chinese era name used during the Three Kingdoms period, specifically associated with the Shu Han state.
-
E.
Wenzheng
Wenzheng is the posthumous honorific title granted to the influential Qing dynasty statesman and military leader Zeng Guofan, reflecting his perceived moral integrity and contributions.
- 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_69e2454b3a5881909c64773dc8a5d289 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1a50fff10819094e71fb0c11b7d95 |
completed | April 29, 2026, 6:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0cdf3454dc819090ca314b8a9b946d |
completed | May 19, 2026, 10:07 p.m. |
| NEDg | Description generation | batch_6a0ce113d3d88190b97af92272a5100f |
completed | May 19, 2026, 10:15 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0ce187efa48190876f67fef4873af2 |
completed | May 19, 2026, 10:17 p.m. |
Created at: April 17, 2026, 5:38 p.m.