Triple
T22298053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deng |
E551174
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Deng Nan |
—
|
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: Deng Nan | Statement: [Deng, hasNotableBearer, Deng Nan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Deng Nan Context triple: [Deng, hasNotableBearer, Deng Nan]
-
A.
Deng Nan
chosen
Deng Nan is a Chinese physicist and politician, known both for her scientific work and for being the daughter of former paramount leader Deng Xiaoping.
-
B.
Deng Yanda
Deng Yanda was a prominent early 20th-century Chinese revolutionary and military leader associated with the left wing of the Kuomintang who advocated for social reform and was executed for his political activities.
-
C.
Deng Hua
Deng Hua was a prominent Chinese military commander and general in the People's Volunteer Army during the Korean War.
-
D.
Deng Yan
Deng Yan is a fictional character played by actress Natasha Liu Bordizzo, best known from the Star Wars series "Ahsoka."
-
E.
Deng Tingzhen
Deng Tingzhen was a prominent Qing dynasty official known for his senior administrative and military leadership in southern China during the early 19th century.
- 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_69e11e45fb848190a1b2ae21296e3a5f |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1572200c88190b9413286136fef15 |
completed | April 29, 2026, 12:56 a.m. |
Created at: April 16, 2026, 8:41 p.m.