Triple
T15476416
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 龍山寺 |
E376790
|
entity |
| Predicate | 所在國家語言環境 |
P103878
|
FINISHED |
| Object | 以華語與臺語為主 |
—
|
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: 以華語與臺語為主 | Statement: [龍山寺, 所在國家語言環境, 以華語與臺語為主]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 所在國家語言環境 Context triple: [龍山寺, 所在國家語言環境, 以華語與臺語為主]
-
A.
nationalLanguageEnvironment
chosen
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.
-
B.
hasLanguageOfSurroundingCountries
Indicates that an entity uses or includes the languages commonly spoken in the countries that geographically surround it.
-
C.
languageOfEnvironment
Indicates the language predominantly used or present in a given environment or context.
-
D.
hasOfficialLanguageOfSurroundingCountry
Indicates that an entity uses as its official language the same language that is official in the country surrounding it.
-
E.
languageUsedInLocality
Indicates that a particular language is used or spoken within a specific locality or geographic area.
- 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_69d85cd21dcc81908646251b1c26ea00 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f88a5dc8190a2d7830748e29180 |
completed | April 16, 2026, 1:46 a.m. |
| PD | Predicate disambiguation | batch_69ded2874b788190999158e0f043be21 |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:34 a.m.