Triple
T16468229
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cheng Qianyun |
E399988
|
entity |
| Predicate | spouseNameInChinese |
P4878
|
FINISHED |
| Object | 刘少奇 |
—
|
LITERAL 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: 刘少奇 | Statement: [Cheng Qianyun, spouseNameInChinese, 刘少奇]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseNameInChinese Context triple: [Cheng Qianyun, spouseNameInChinese, 刘少奇]
-
A.
spouseNameInPinyin
Indicates that it specifies the spouse’s name written in Pinyin (the Romanized form of Chinese characters).
-
B.
spouseNameInVietnamese
Indicates that the predicate specifies the name of a person's spouse as written or expressed in the Vietnamese language.
-
C.
spouseRealName
Indicates that one person is the legally recognized spouse of another, using the spouse’s real (non-alias) name.
-
D.
spouse name
Indicates that one entity is the legally recognized husband or wife of the other, specifying the partner’s name in a marital relationship.
-
E.
nameInChinese
chosen
Indicates that an entity has a specific written name or label expressed in the Chinese language.
- F. None of above.
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_69d87f2dac988190b74d6e185fa88ba4 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32dce342081909cad56dc92de13a2 |
completed | April 18, 2026, 7:07 a.m. |
| PD | Predicate disambiguation | batch_69e227048d608190a4205eae3117629a |
completed | April 17, 2026, 12:26 p.m. |
Created at: April 10, 2026, 5:11 a.m.