Triple
T114442
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beijing |
E2312
|
entity |
| Predicate | dialect |
P1762
|
FINISHED |
| Object | Beijing dialect of Mandarin |
—
|
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: Beijing dialect of Mandarin | Statement: [Beijing, dialect, Beijing dialect of Mandarin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dialect Context triple: [Beijing, dialect, Beijing dialect of Mandarin]
-
A.
regionalDialect
chosen
Indicates that one entity uses or is associated with a dialect specific to a particular geographic region in relation to another entity.
-
B.
deFactoLanguage
Indicates that a language is used in practice as the primary or common language in a context, even if it has no official legal status there.
-
C.
nativeLanguage
Indicates the language that a person or entity originally learned and uses as their primary or first language.
-
D.
recognizedRegionalLanguage
Indicates that a language holds officially recognized status within a specific region or subnational jurisdiction.
-
E.
historicallySpokenIn
Indicates that a language was used for spoken communication in a particular place or region during a past historical period.
- 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_69a24fcdaeb48190a2d796677e4b3281 |
completed | Feb. 28, 2026, 2:15 a.m. |
| NER | Named-entity recognition | batch_69a25760af348190bf402089c240887d |
completed | Feb. 28, 2026, 2:48 a.m. |
| PD | Predicate disambiguation | batch_69a2564417848190a8a8a38e97348963 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:20 a.m.