Triple
T18205192
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wav2Vec2 |
E435883
|
entity |
| Predicate | usesMasking |
P130223
|
FINISHED |
| Object | time-step masking on latent speech representations |
—
|
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: time-step masking on latent speech representations | Statement: [Wav2Vec2, usesMasking, time-step masking on latent speech representations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesMasking Context triple: [Wav2Vec2, usesMasking, time-step masking on latent speech representations]
-
A.
usesMaskIn
Indicates that an entity wears or employs a mask while present in or interacting within a specified context or location.
-
B.
mayMask
Indicates that one entity is permitted or able to conceal, obscure, or hide another entity or its properties.
-
C.
usesMasksOrDisguises
Indicates that an entity employs masks, costumes, or other forms of disguise to conceal or alter its identity in the context of an action or interaction.
-
D.
typicalMaskType
Indicates the kind or category of mask that is most commonly or characteristically used or associated with an entity.
-
E.
mask
Indicates that one entity covers, conceals, or obscures another entity, typically to hide its identity, appearance, or specific features.
- 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_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. |
| PDg | Predicate description generation | batch_69e438f684e48190b38c64b58c518b6a |
completed | April 19, 2026, 2:07 a.m. |
Created at: April 10, 2026, 10:32 a.m.