Triple
T7947136
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 985工程 |
E184524
|
entity |
| Predicate | 政策影响 |
P22482
|
FINISHED |
| Object | 推动中国一流大学建设进程 |
—
|
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: 推动中国一流大学建设进程 | Statement: [985工程, 政策影响, 推动中国一流大学建设进程]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 政策影响 Context triple: [985工程, 政策影响, 推动中国一流大学建设进程]
-
A.
effectOnInstitution
Indicates the impact or influence that one entity, event, or action has on an institution’s state, functioning, or outcomes.
-
B.
impactOnBusiness
Indicates the effect or influence that one factor, event, or action has on a business’s performance, operations, or outcomes.
-
C.
impactOnIndustry
Indicates the effect or influence that one entity, event, or action has on the state, performance, or development of an industry.
-
D.
influencedPolicyArea
chosen
Indicates that one entity has affected, shaped, or guided the development, direction, or implementation of a particular policy area associated with another entity.
-
E.
impactOnLaw
Indicates the effect or influence that one entity, event, or action has on laws, legal rules, or the legal system.
- 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_69ca8291c2008190b1b8832c87814bcf |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3b2abdbc819085ae53826d36af3b |
completed | March 31, 2026, 3:10 a.m. |
| PD | Predicate disambiguation | batch_69cae9361bc48190886b7681e563d46b |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 5:09 p.m.