Triple
T31849104
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Utsat people |
E813016
|
entity |
| Predicate | linguisticInfluenceOnLanguage |
P23173
|
FINISHED |
| Object | Chinese loanwords in Tsat |
—
|
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: Chinese loanwords in Tsat | Statement: [Utsat people, linguisticInfluenceOnLanguage, Chinese loanwords in Tsat]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: linguisticInfluenceOnLanguage Context triple: [Utsat people, linguisticInfluenceOnLanguage, Chinese loanwords in Tsat]
-
A.
linguisticInfluence
Indicates that one entity has affected, shaped, or contributed to the language, style, or linguistic features of another entity.
-
B.
languageInfluence
chosen
Indicates that one language has an effect on the development, usage, or characteristics of another language.
-
C.
shareLanguageInfluence
Indicates that two entities affect or shape each other’s language use, development, or characteristics through mutual or shared influence.
-
D.
influencesLanguageOf
Indicates that one entity affects, shapes, or alters the language used by another entity.
-
E.
influencedLanguage
Indicates that one language has had an effect on the development, structure, or usage of another language.
- 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_69f348eb327881909b4584b925742f6e |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6b03aa0d88190bf436695207e35c7 |
completed | May 3, 2026, 2:17 a.m. |
| PD | Predicate disambiguation | batch_69f6aca59d4881908d14ed47962703bd |
completed | May 3, 2026, 2:02 a.m. |
Created at: April 30, 2026, 11:51 p.m.