Triple
T18364950
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Empress Xiaoyichun |
E440018
|
entity |
| Predicate | motherOf |
P120
|
FINISHED |
| Object | Yongqi |
—
|
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: Yongqi | Statement: [Empress Xiaoyichun, motherOf, Yongqi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yongqi Context triple: [Empress Xiaoyichun, motherOf, Yongqi]
-
A.
Yongqi
chosen
Yongqi was a Qing dynasty imperial prince, noted as one of the most talented sons of the Qianlong Emperor before his early death.
-
B.
Wuling
Wuling is the historical name of a region in Hunan, China, that later became known as Changde.
-
C.
Wuling
Wuling is a Chinese automotive marque known for producing affordable compact cars and microvans, marketed through a joint venture involving General Motors.
-
D.
Fuxing
Fuxing is a series of Chinese high-speed electric multiple unit trains known for their advanced technology, high operating speeds, and use on major routes across China’s high-speed rail network.
-
E.
Guangqi
Guangqi was the era name used during part of Emperor Xizong of the Tang dynasty’s reign in late ninth-century China.
- 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_69d8b918221c8190a9f7b563d64ac677 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e5174d31608190851a5bab6878c203 |
completed | April 19, 2026, 5:56 p.m. |
Created at: April 10, 2026, 10:38 a.m.