Triple
T7947155
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 985工程 |
E184524
|
entity |
| Predicate | 语言环境 |
P19095
|
FINISHED |
| Object | 汉语 |
—
|
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: 汉语 | Statement: [985工程, 语言环境, 汉语]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 语言环境 Context triple: [985工程, 语言环境, 汉语]
-
A.
languageOfEnvironment
chosen
Indicates the language predominantly used or present in a given environment or context.
-
B.
sociolinguisticSituation
Indicates the social and cultural context in which language is used, including factors like participants, setting, norms, and power relations that shape linguistic behavior.
-
C.
languageArea
Indicates the geographic or cultural region in which a particular language is used or predominantly spoken.
-
D.
languageOfInstitutionalContext
Indicates the language used as the primary medium of communication within an institutional setting or context.
-
E.
languageContactWith
Indicates a relationship where two or more languages come into contact through their speakers, leading to interaction and potential mutual influence.
- 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.