Triple
T23288489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daoguang Emperor |
E589958
|
entity |
| Predicate | personalName |
P24312
|
FINISHED |
| Object | Mianning |
—
|
NE NERFINISHED |
How this triple was built (2 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: Mianning | Statement: [Daoguang Emperor, personalName, Mianning]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mianning Context triple: [Daoguang Emperor, personalName, Mianning]
-
A.
Mianning
chosen
Mianning was the personal name of the Daoguang Emperor, a Qing dynasty ruler of China in the early to mid-19th century.
-
B.
Mengjiang
Mengjiang was a Japanese puppet state established in Inner Mongolia during the Second Sino-Japanese War and World War II.
-
C.
Mianning County
Mianning County is an administrative region in southern Sichuan Province, China, known for its mountainous terrain, ethnic diversity, and role as a cultural area for several Tibeto-Burman-speaking communities.
-
D.
Muzhou
Muzhou is a notable town within Xinhui District in Jiangmen, Guangdong Province, China.
-
E.
Raoping
Raoping is a coastal county in eastern Guangdong, China, known for its Teochew culture and strategic location along major transport routes.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e25d1af9d88190a0b9b5e8fa608618 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f19649570c8190b565fafa55b1f886 |
completed | April 29, 2026, 5:25 a.m. |
Created at: April 17, 2026, 5:01 p.m.