Triple
T18205182
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wav2Vec2 |
E435883
|
entity |
| Predicate | achievesStateOfTheArtOn |
P97552
|
FINISHED |
| Object | LibriSpeech 100h setting (at time of publication) |
—
|
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: LibriSpeech 100h setting (at time of publication) | Statement: [Wav2Vec2, achievesStateOfTheArtOn, LibriSpeech 100h setting (at time of publication)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: achievesStateOfTheArtOn Context triple: [Wav2Vec2, achievesStateOfTheArtOn, LibriSpeech 100h setting (at time of publication)]
-
A.
isStateOfTheArt
chosen
Indicates that something represents the most advanced, up-to-date, or cutting-edge level of development or performance in its field.
-
B.
usesNeuralNetworks
Indicates that one entity employs neural network models or techniques as part of its functioning, processing, or decision-making.
-
C.
AIPTechnology
Indicates a relationship where an artificial intelligence platform or system is based on, utilizes, or is characterized by a specific technology.
-
D.
equipmentTypeTrainedOn
Indicates the type of equipment on which an entity has received training or is qualified to operate.
-
E.
trainingObjective
Indicates the goal or target outcome that a training process is designed to achieve.
- 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.