Triple
T1430313
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mao Fumei |
E30428
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Mao Fumei |
E30428
|
NE FINISHED |
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: Mao Fumei | Statement: [Mao Fumei, name, Mao Fumei]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mao Fumei Context triple: [Mao Fumei, name, Mao Fumei]
-
A.
Mao Fumei
chosen
Mao Fumei was the first wife of Chinese Nationalist leader Chiang Kai-shek and the mother of his son Chiang Ching-kuo.
-
B.
Mao Xinyu
Mao Xinyu is a Chinese major general and military historian best known as the grandson of Mao Zedong.
-
C.
Wang Jingjiu
Wang Jingjiu was a Chinese military officer best known for commanding the National Revolutionary Army’s 87th Division during the Republican era.
-
D.
Mao Anqing
Mao Anqing was the second son of Chinese Communist leader Mao Zedong, known for his work as a Russian-language translator and his relatively low political profile compared to his father.
-
E.
He Yingqin
He Yingqin was a prominent Chinese Nationalist general and politician who played key military and governmental roles during the Republic of China era, particularly in the early stages of the Second Sino-Japanese War.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a498fc69ec8190b61722bd4b67c4d2 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c4db2d7481908d241593d0e17d83 |
completed | March 1, 2026, 10:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad016930ec8190ab3900d6f40c4aa0 |
completed | March 8, 2026, 4:56 a.m. |
Created at: March 1, 2026, 8 p.m.