Triple
T10109997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 湘语 |
E218213
|
entity |
| Predicate | standardLanguageInfluence |
P6195
|
FINISHED |
| Object | 受普通话推广影响较大 |
—
|
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: 受普通话推广影响较大 | Statement: [湘语, standardLanguageInfluence, 受普通话推广影响较大]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: standardLanguageInfluence Context triple: [湘语, standardLanguageInfluence, 受普通话推广影响较大]
-
A.
languageInfluence
Indicates that one language has an effect on the development, usage, or characteristics of another language.
-
B.
influencedLanguage
Indicates that one language has had an effect on the development, structure, or usage of another language.
-
C.
languageOfInfluence
Indicates a relationship where one language has influenced the development, usage, or characteristics of another language.
-
D.
shareLanguageInfluence
Indicates that two entities affect or shape each other’s language use, development, or characteristics through mutual or shared influence.
-
E.
languageStandard
chosen
Indicates that one entity conforms to, is defined by, or is governed by the language rules, specifications, or conventions established by another entity as a standard.
- 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_69ca83da93fc8190b54e44bc2b34857c |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cdd0cdb3c88190a74f75bf865664f3 |
completed | April 2, 2026, 2:13 a.m. |
| PD | Predicate disambiguation | batch_69cd4b9ed7e48190aa132ef8a69b49f9 |
completed | April 1, 2026, 4:45 p.m. |
Created at: March 30, 2026, 9:03 p.m.