Triple
T35620663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CCIR System G |
E1029302
|
entity |
| Predicate | audioPreemphasis |
P152853
|
FINISHED |
| Object | 50 µs |
—
|
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: 50 µs | Statement: [CCIR System G, audioPreemphasis, 50 µs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: audioPreemphasis Context triple: [CCIR System G, audioPreemphasis, 50 µs]
-
A.
audioProcessing
Indicates that one entity performs operations to analyze, modify, or transform audio data associated with another entity.
-
B.
audioEffect
chosen
Indicates that one entity applies or represents an audio processing effect that modifies the sound characteristics of another entity.
-
C.
audioStandard
Indicates the audio format or specification standard that applies to the associated media or device.
-
D.
audioModulation
Indicates a relationship where one audio signal or parameter is used to vary or control another audio signal’s characteristics (such as amplitude, frequency, or timbre) over time.
-
E.
usesPsychoacousticModel
Indicates that one entity applies a psychoacoustic model to analyze, process, or optimize audio based on human auditory perception.
- 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_69f76e0709408190bbe322bf1707ef6b |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f79ef1914c8190a293c37fef343a6d |
completed | May 3, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_69f79e4bdbcc8190be7a0d2cf8a77b64 |
completed | May 3, 2026, 7:13 p.m. |
Created at: May 3, 2026, 4:05 p.m.