Triple
T21568266
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nuance Communications |
E532215
|
entity |
| Predicate | languageTechnologyDomain |
P144278
|
FINISHED |
| Object | automatic speech recognition |
—
|
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: automatic speech recognition | Statement: [Nuance Communications, languageTechnologyDomain, automatic speech recognition]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageTechnologyDomain Context triple: [Nuance Communications, languageTechnologyDomain, automatic speech recognition]
-
A.
languageProvision
Indicates that one entity supplies, supports, or makes available a particular language (or set of languages) for use by another entity.
-
B.
languageText
Indicates that a piece of text is expressed in, or associated with, a particular language.
-
C.
languageModality
Indicates the mode or form in which a language is expressed or perceived (e.g., spoken, signed, written, or tactile).
-
D.
languageCategory
Indicates the classification relationship where a language is assigned to a particular linguistic or functional category.
-
E.
languageCapacity
Indicates the extent to which an entity is able to understand, produce, or otherwise use language.
- F. None of above. chosen
Provenance (4 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_69e0c460db088190828c64206a450273 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eee9cad95c8190ab165ced6f242302 |
completed | April 27, 2026, 4:44 a.m. |
| PD | Predicate disambiguation | batch_69e6320c8c2c81908bf031447d66a052 |
completed | April 20, 2026, 2:02 p.m. |
| PDg | Predicate description generation | batch_69e633bf34c481909925d8dc1a633a65 |
completed | April 20, 2026, 2:10 p.m. |
Created at: April 16, 2026, 6:30 p.m.