Triple
T9051008
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Muong |
E216881
|
entity |
| Predicate | languageRelation |
P10003
|
FINISHED |
| Object | closely related to Vietnamese language |
—
|
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: closely related to Vietnamese language | Statement: [Muong, languageRelation, closely related to Vietnamese language]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageRelation Context triple: [Muong, languageRelation, closely related to Vietnamese language]
-
A.
semanticRelation
Indicates a general meaning-based connection between two entities, such as similarity, implication, or conceptual association.
-
B.
linguisticallyRelatedTo
chosen
Indicates that two entities are connected through a linguistic relationship, such as sharing a common language, origin, structure, or other language-based association.
-
C.
termRelationTo
Indicates a general relational association between one term and another, without specifying the exact nature of that relationship.
-
D.
subjectRelation
Indicates that one entity stands in a specified relational role or connection to another entity.
-
E.
titleRelation
Indicates a relationship where one entity serves as the title, designation, or formal name associated with another entity.
- 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_69ca83d362e88190ae44b4e4dc194209 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc6b54423081908d9fd985109e336a |
completed | April 1, 2026, 12:48 a.m. |
| PD | Predicate disambiguation | batch_69cc5ee566b081909e3cdaf551dbd0ec |
completed | March 31, 2026, 11:55 p.m. |
Created at: March 30, 2026, 7:10 p.m.