Triple
T6350127
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daoguang Emperor |
E142847
|
entity |
| Predicate | courtesyName |
P570
|
FINISHED |
| Object |
Licheng
Licheng is the courtesy name of the Daoguang Emperor, a Qing dynasty ruler of China in the early 19th century.
|
E589958
|
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: Licheng | Statement: [Daoguang Emperor, courtesyName, Licheng]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Licheng Context triple: [Daoguang Emperor, courtesyName, Licheng]
-
A.
Yuncheng
Yuncheng is a major city in southern Shanxi Province, China, known for its historical sites and role as a regional transportation and economic hub.
-
B.
Jianye
Jianye is an ancient name for the city now known as Nanjing, a historically significant capital in several Chinese dynasties.
-
C.
Yongcheng
Yongcheng was a Qing dynasty imperial prince, known as one of the sons of the Qianlong Emperor of China.
-
D.
Luzhi
Luzhi is an ancient canal town near Suzhou in China, renowned for its well-preserved waterways, stone bridges, and traditional Jiangnan architecture.
-
E.
Jianyang
Jianyang is a county-level city in northern Fujian Province, China, known for its historical role in tea production and its location along the Min River.
- 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: Licheng Triple: [Daoguang Emperor, courtesyName, Licheng]
Generated description
Licheng is the courtesy name of the Daoguang Emperor, a Qing dynasty ruler of China in the early 19th century.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Licheng Target entity description: Licheng is the courtesy name of the Daoguang Emperor, a Qing dynasty ruler of China in the early 19th century.
-
A.
Yuncheng
Yuncheng is a major city in southern Shanxi Province, China, known for its historical sites and role as a regional transportation and economic hub.
-
B.
Jianye
Jianye is an ancient name for the city now known as Nanjing, a historically significant capital in several Chinese dynasties.
-
C.
Yongcheng
Yongcheng was a Qing dynasty imperial prince, known as one of the sons of the Qianlong Emperor of China.
-
D.
Luzhi
Luzhi is an ancient canal town near Suzhou in China, renowned for its well-preserved waterways, stone bridges, and traditional Jiangnan architecture.
-
E.
Jianyang
Jianyang is a county-level city in northern Fujian Province, China, known for its historical role in tea production and its location along the Min River.
- 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_69c008d6dcbc8190aa1c2f1fd8916b42 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c067bcec2c8190bb383605847b0f0b |
completed | March 22, 2026, 10:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6386784008190b0ac82804a4ee30e |
completed | March 27, 2026, 7:57 a.m. |
| NEDg | Description generation | batch_69c639eabaf88190bf81112cde6e8e99 |
completed | March 27, 2026, 8:03 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c63a8738ac8190af0bface4b18eea6 |
completed | March 27, 2026, 8:06 a.m. |
Created at: March 22, 2026, 4:31 p.m.