Triple
T31008277
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hu Minghua |
E790133
|
entity |
| Predicate | nameInChinesePinyin |
P41219
|
FINISHED |
| Object | Hu Minghua |
—
|
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: Hu Minghua | Statement: [Hu Minghua, nameInChinesePinyin, Hu Minghua]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nameInChinesePinyin Context triple: [Hu Minghua, nameInChinesePinyin, Hu Minghua]
-
A.
nameInChinese
Indicates that an entity has a specific written name or label expressed in the Chinese language.
-
B.
bearerNameInPinyin
Indicates that the bearer’s name is represented in its pinyin (romanized Chinese) form.
-
C.
ChinesePinyin
chosen
Indicates that one entity is the Chinese pinyin (romanized phonetic transcription) representation of another entity.
-
D.
spouseNameInPinyin
Indicates that it specifies the spouse’s name written in Pinyin (the Romanized form of Chinese characters).
-
E.
nameOrderInChinese
Indicates that the entities are arranged in the order that personal names are written or spoken in Chinese (family name first, given name second).
- 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_69f224c73ca48190a1e46cb58ad4045b |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f6a916d2e08190bafc01cba73b6469 |
completed | May 3, 2026, 1:47 a.m. |
| PD | Predicate disambiguation | batch_69f6a7548eb48190a69b60a3c6ad53b9 |
completed | May 3, 2026, 1:39 a.m. |
Created at: April 29, 2026, 8:57 p.m.