Triple
T18204823
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | XLM-R |
E435876
|
entity |
| Predicate | languageModelType |
P7183
|
FINISHED |
| Object | encoder-only transformer |
—
|
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: encoder-only transformer | Statement: [XLM-R, languageModelType, encoder-only transformer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageModelType Context triple: [XLM-R, languageModelType, encoder-only transformer]
-
A.
languageModel
Indicates that one entity is a language model used to process, generate, or understand natural language in relation to another entity.
-
B.
hasLanguageModel
chosen
Indicates that an entity possesses, uses, or is associated with a particular language model.
-
C.
languageModality
Indicates the mode or form in which a language is expressed or perceived (e.g., spoken, signed, written, or tactile).
-
D.
linguisticType
Indicates the type or category of language or linguistic system associated with an entity (e.g., spoken, signed, written, or other linguistic modality).
-
E.
languageProvision
Indicates that one entity supplies, supports, or makes available a particular language (or set of languages) for use by 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_69d8b90dba6481908e119eb9aa4ca0cb |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e222831081908f7d5500424e3acb |
completed | April 19, 2026, 2:09 p.m. |
| PD | Predicate disambiguation | batch_69e4332155d88190b106d0dceb4554af |
completed | April 19, 2026, 1:42 a.m. |
Created at: April 10, 2026, 10:32 a.m.