Triple
T3047540
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | QuietTuning noise reduction |
E83485
|
entity |
| Predicate | targetsNoiseType |
P45319
|
FINISHED |
| Object | road noise |
—
|
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: road noise | Statement: [QuietTuning noise reduction, targetsNoiseType, road noise]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetsNoiseType Context triple: [QuietTuning noise reduction, targetsNoiseType, road noise]
-
A.
noiseLevel
Indicates the intensity or amount of sound present in a given environment or from a specific source.
-
B.
hasNoiseTerm
Indicates that a given expression, model, or equation includes an additional noise term representing random or unexplained variation.
-
C.
signalType
Indicates the specific kind or category of signal associated with or used by an entity or interaction.
-
D.
detectorType
Indicates the specific kind or category of detector associated with an entity or measurement.
-
E.
noiseReductionGoal
Indicates the intended target level or objective for reducing noise in a given context or system.
- 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_69ad8b24924c8190a9bb6f61d519e4ae |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9bad11b48190a5bd01e91a320e14 |
completed | March 8, 2026, 3:54 p.m. |
| PD | Predicate disambiguation | batch_69ad962195388190856013a2519c2b0f |
completed | March 8, 2026, 3:30 p.m. |
| PDg | Predicate description generation | batch_69ad9b272a408190ba0ce09bdeea76c9 |
completed | March 8, 2026, 3:52 p.m. |
Created at: March 8, 2026, 3:01 p.m.