Triple
T14565530
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 東沙環礁 |
E341773
|
entity |
| Predicate | 語言環境 |
P19095
|
FINISHED |
| Object | 主要使用中文（國語、臺灣常用語） |
—
|
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: 主要使用中文（國語、臺灣常用語） | Statement: [東沙環礁, 語言環境, 主要使用中文（國語、臺灣常用語）]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 語言環境 Context triple: [東沙環礁, 語言環境, 主要使用中文（國語、臺灣常用語）]
-
A.
languageOfEnvironment
chosen
Indicates the language predominantly used or present in a given environment or context.
-
B.
languageIndependence
Indicates that a concept, method, or representation does not depend on any specific programming or natural language and can be applied uniformly across different languages.
-
C.
nationalLanguageEnvironment
Indicates the relationship between a country or region and the language(s) that function as the primary or officially recognized means of communication in that environment.
-
D.
languageShift
Indicates a change in the primary language used by an entity, such as switching from one language to another over time or in a given context.
-
E.
languageCategory
Indicates the classification relationship where a language is assigned to a particular linguistic or functional category.
- 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_69d822dcc6248190bed689984bceb0e2 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb38c6350819091090ffd15772f6f |
completed | April 14, 2026, 9:37 p.m. |
| PD | Predicate disambiguation | batch_69de5c57489c8190b57917be1dba6ae6 |
completed | April 14, 2026, 3:25 p.m. |
Created at: April 10, 2026, 1:23 a.m.