Triple
T30151146
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Double First Class University Plan |
E766397
|
entity |
| Predicate | officialTermInChinese |
P35123
|
FINISHED |
| Object | 世界一流大学和一流学科建设 |
—
|
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: 世界一流大学和一流学科建设 | Statement: [Double First Class University Plan, officialTermInChinese, 世界一流大学和一流学科建设]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: officialTermInChinese Context triple: [Double First Class University Plan, officialTermInChinese, 世界一流大学和一流学科建设]
-
A.
ChineseAbbreviation
Indicates that one term is an abbreviation or shortened form of another term in Chinese.
-
B.
usedChineseTermForGod
Indicates that an entity referred to or addressed God using a specifically Chinese linguistic term or name.
-
C.
officialTermLanguage
chosen
Indicates the language in which an official term is formally expressed or defined.
-
D.
hasMeaningInChinese
Indicates that one entity (such as a word, phrase, or symbol) possesses a specific meaning or interpretation within the Chinese language.
-
E.
terminologyOf
Indicates that one entity provides or defines the specialized terms or vocabulary used within the context of another entity.
- 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_69f22479cd088190ab4c6f3fce39d1c5 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fe6e492bf8819080b25221d13445ea |
completed | May 8, 2026, 11:14 p.m. |
| PD | Predicate disambiguation | batch_69fe6dd33a6881908fe9bbbc184cab51 |
completed | May 8, 2026, 11:12 p.m. |
Created at: April 29, 2026, 7:20 p.m.