Triple
T2849609
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dolby A-type noise reduction |
E63060
|
entity |
| Predicate | noiseReductionAmount |
P26462
|
FINISHED |
| Object | up to about 10 dB per band |
—
|
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: up to about 10 dB per band | Statement: [Dolby A-type noise reduction, noiseReductionAmount, up to about 10 dB per band]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: noiseReductionAmount Context triple: [Dolby A-type noise reduction, noiseReductionAmount, up to about 10 dB per band]
-
A.
noiseReductionGoal
chosen
Indicates the intended target level or objective for reducing noise in a given context or system.
-
B.
noiseLevel
Indicates the intensity or amount of sound present in a given environment or from a specific source.
-
C.
hasNoiseTerm
Indicates that a given expression, model, or equation includes an additional noise term representing random or unexplained variation.
-
D.
reducedPowerOf
Indicates that one entity has a diminished or weakened level of power, authority, or effectiveness relative to another or to its previous state.
-
E.
reduces
Indicates that one entity causes a decrease in the amount, intensity, degree, or impact of 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_69ab4c407c408190857d25e027155ce9 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdf41ac24819087c5b72e3b84117c |
completed | March 7, 2026, 8:18 a.m. |
| PD | Predicate disambiguation | batch_69abdd0e86808190bcefffafbd3cd441 |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 10:02 p.m.