Triple
T31276754
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Changsha dialect |
E797540
|
entity |
| Predicate | mainLocalVernacularOf |
P94547
|
FINISHED |
| Object | urban Changsha |
—
|
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: urban Changsha | Statement: [Changsha dialect, mainLocalVernacularOf, urban Changsha]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainLocalVernacularOf Context triple: [Changsha dialect, mainLocalVernacularOf, urban Changsha]
-
A.
vernacularOf
chosen
Indicates that one language or dialect is the everyday, locally used form corresponding to another, more general or standard language.
-
B.
languageUsedInLocality
Indicates that a particular language is used or spoken within a specific locality or geographic area.
-
C.
localLanguageName
Indicates the name of a language as it is written or referred to in its own local or native form.
-
D.
vernacularGroup
Indicates a relationship where entities are grouped or associated based on sharing the same vernacular (local or commonly spoken) language.
-
E.
languageOfLocalOrganization
Indicates the language used or officially adopted by a local organization in its operations or communications.
- 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_69f224def9088190a37034eab3daf57f |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f6b967d5308190bbb66d0a8dd52612 |
completed | May 3, 2026, 2:56 a.m. |
| PD | Predicate disambiguation | batch_69f6b6293188819080d5041ca0adb969 |
completed | May 3, 2026, 2:42 a.m. |
Created at: April 29, 2026, 9:13 p.m.