Triple
T30350523
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sony WH-1000XM5 |
E771976
|
entity |
| Predicate | noiseCancellingType |
P107588
|
FINISHED |
| Object | hybrid ANC |
—
|
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: hybrid ANC | Statement: [Sony WH-1000XM5, noiseCancellingType, hybrid ANC]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: noiseCancellingType Context triple: [Sony WH-1000XM5, noiseCancellingType, hybrid ANC]
-
A.
usesCrosstalkCancellation
Indicates that one entity applies crosstalk cancellation techniques to reduce or eliminate interference between signals associated with another entity.
-
B.
usesEchoCancellation
Indicates that an entity employs echo cancellation techniques to reduce or eliminate echo in audio communication.
-
C.
hasNoiseModes
Indicates that an entity supports or is associated with one or more distinct noise-related operating modes or settings.
-
D.
noiseReductionType
chosen
Indicates the specific method or technique used to reduce or minimize noise in a given context.
-
E.
hasNoisePerformance
Indicates the degree to which one entity’s operation or behavior produces or is characterized by a certain level or quality of noise.
- 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_69f2248b9a208190bc3e6804acd5afd6 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6820b4b8c81908f5bbae956565ec0 |
completed | May 2, 2026, 11 p.m. |
| PD | Predicate disambiguation | batch_69f678d019fc8190913662cd2f87b857 |
completed | May 2, 2026, 10:21 p.m. |
Created at: April 29, 2026, 7:56 p.m.