Triple
T12935113
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dolby A |
E309487
|
entity |
| Predicate | noiseReductionType |
P107588
|
FINISHED |
| Object | compander |
—
|
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: compander | Statement: [Dolby A, noiseReductionType, compander]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: noiseReductionType Context triple: [Dolby A, noiseReductionType, compander]
-
A.
noiseReductionFeature
Indicates that an entity includes or supports a capability to reduce or minimize unwanted noise.
-
B.
noiseReductionGoal
Indicates the intended target level or objective for reducing noise in a given context or system.
-
C.
hasGreaterNoiseReductionThan
Indicates that one entity provides a higher level of noise reduction compared to another entity.
-
D.
hasNoiseModes
Indicates that an entity supports or is associated with one or more distinct noise-related operating modes or settings.
-
E.
targetsNoiseType
Indicates that an entity is directed at, designed for, or specifically affects a particular type or category of noise.
- 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_69d7bdfa933c8190b5a27aa4a08a19b7 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97e59a4c88190907d05b8d57dae89 |
completed | April 10, 2026, 10:48 p.m. |
| PD | Predicate disambiguation | batch_69d97db69f548190a1a693bc0d6c191a |
completed | April 10, 2026, 10:46 p.m. |
| PDg | Predicate description generation | batch_69d97e5811f481908178fac6d2e0efcd |
completed | April 10, 2026, 10:48 p.m. |
Created at: April 9, 2026, 5:42 p.m.