Triple
T15430936
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Empress Xiaochengjing |
E369636
|
entity |
| Predicate | posthumousName |
P744
|
FINISHED |
| Object |
Xiaochengjing
Xiaochengjing was a Ming dynasty empress of China, honored posthumously for her virtue and status as a principal consort of the emperor.
|
E1155631
|
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: Xiaochengjing | Statement: [Empress Xiaochengjing, posthumousName, Xiaochengjing]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Xiaochengjing Context triple: [Empress Xiaochengjing, posthumousName, Xiaochengjing]
-
A.
Qiaocheng
Qiaocheng is a historical name for the area now known as Bozhou, a city in Anhui Province, China, with a long history and cultural significance.
-
B.
Shangyuan
Shangyuan was a Chinese imperial era name used during the reign of Emperor Suzong of the Tang dynasty.
-
C.
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.
-
D.
Chengqu
Chengqu is the central urban district and administrative hub of Jincheng in Shanxi Province, China.
-
E.
Licheng
Licheng is the courtesy name of the Daoguang Emperor, a Qing dynasty ruler of China in the early 19th century.
- 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: Xiaochengjing Triple: [Empress Xiaochengjing, posthumousName, Xiaochengjing]
Generated description
Xiaochengjing was a Ming dynasty empress of China, honored posthumously for her virtue and status as a principal consort of the emperor.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Xiaochengjing Target entity description: Xiaochengjing was a Ming dynasty empress of China, honored posthumously for her virtue and status as a principal consort of the emperor.
-
A.
Qiaocheng
Qiaocheng is a historical name for the area now known as Bozhou, a city in Anhui Province, China, with a long history and cultural significance.
-
B.
Shangyuan
Shangyuan was a Chinese imperial era name used during the reign of Emperor Suzong of the Tang dynasty.
-
C.
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.
-
D.
Chengqu
Chengqu is the central urban district and administrative hub of Jincheng in Shanxi Province, China.
-
E.
Licheng
Licheng is the courtesy name of the Daoguang Emperor, a Qing dynasty ruler of China in the early 19th century.
- 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_69d85a1849f48190bf898068b2806fae |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03ed8ea888190bff8dc14859cca31 |
completed | April 16, 2026, 1:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff1a827d9081909fabc48bc685ba5b |
completed | May 9, 2026, 11:29 a.m. |
| NEDg | Description generation | batch_69ff1b4c13e08190b2ccee59da02d0ae |
completed | May 9, 2026, 11:32 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff1bde8914819087d5d2ac88de34aa |
completed | May 9, 2026, 11:34 a.m. |
Created at: April 10, 2026, 3:21 a.m.