Triple
T27542059
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 客家话 |
E695260
|
entity |
| Predicate | 与普通话关系 |
P50775
|
FINISHED |
| Object | 与普通话在语音和词汇上存在较大差异 |
—
|
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: 与普通话在语音和词汇上存在较大差异 | Statement: [客家话, 与普通话关系, 与普通话在语音和词汇上存在较大差异]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 与普通话关系 Context triple: [客家话, 与普通话关系, 与普通话在语音和词汇上存在较大差异]
-
A.
linguisticallyRelatedTo
Indicates that two entities are connected through a linguistic relationship, such as sharing a common language, origin, structure, or other language-based association.
-
B.
closelyAssociatedLanguage
Indicates that one language is closely connected to another, such as through frequent co-use, mutual influence, or strong cultural or regional association.
-
C.
hasRelatedLanguage
Indicates that one language is related to another through shared linguistic origins, features, or classification.
-
D.
phonologyRelation
chosen
Indicates a relationship between linguistic elements based on their phonological properties, such as sound patterns, features, or structures.
-
E.
languageFamilyRelation
Indicates a relationship where one language belongs to, descends from, or is otherwise classified within a particular language family.
- 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_69ef5386c3e08190bfe33aa326e1f72b |
completed | April 27, 2026, 12:16 p.m. |
| NER | Named-entity recognition | batch_69f62f5ec8b481909241271f7d602dc9 |
completed | May 2, 2026, 5:07 p.m. |
| PD | Predicate disambiguation | batch_69f623ac3a9c8190a6ee0c137b09e4b0 |
completed | May 2, 2026, 4:17 p.m. |
Created at: April 27, 2026, 1:31 p.m.