Triple
T6398128
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | IRCAM |
E143990
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
AudioSculpt
AudioSculpt is a sound analysis and processing software developed by IRCAM, widely used for detailed spectral editing and transformation of audio.
|
E590929
|
NE FINISHED |
How this triple was built (4 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: AudioSculpt | Statement: [IRCAM, notableWork, AudioSculpt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AudioSculpt Context triple: [IRCAM, notableWork, AudioSculpt]
-
A.
Audion
Audion is an early triode vacuum tube invented by Lee de Forest that enabled the amplification of electrical signals and was crucial to the development of radio and electronics.
-
B.
WaveGlow
WaveGlow is a flow-based generative neural network model for fast, high-quality text-to-speech audio synthesis.
-
C.
audioOS
audioOS is Apple’s specialized operating system designed to power and manage the features of its HomePod smart speakers.
-
D.
Auto-Tune
Auto-Tune is an audio processing technology that automatically corrects or stylizes vocal pitch, widely used in music production for both subtle tuning and distinctive robotic effects.
-
E.
Audacity
Audacity is an American garage punk band known for its energetic, lo-fi sound and association with the Southern California DIY scene.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: AudioSculpt Triple: [IRCAM, notableWork, AudioSculpt]
Generated description
AudioSculpt is a sound analysis and processing software developed by IRCAM, widely used for detailed spectral editing and transformation of audio.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: AudioSculpt Target entity description: AudioSculpt is a sound analysis and processing software developed by IRCAM, widely used for detailed spectral editing and transformation of audio.
-
A.
Audion
Audion is an early triode vacuum tube invented by Lee de Forest that enabled the amplification of electrical signals and was crucial to the development of radio and electronics.
-
B.
WaveGlow
WaveGlow is a flow-based generative neural network model for fast, high-quality text-to-speech audio synthesis.
-
C.
audioOS
audioOS is Apple’s specialized operating system designed to power and manage the features of its HomePod smart speakers.
-
D.
Auto-Tune
Auto-Tune is an audio processing technology that automatically corrects or stylizes vocal pitch, widely used in music production for both subtle tuning and distinctive robotic effects.
-
E.
Audacity
Audacity is an American garage punk band known for its energetic, lo-fi sound and association with the Southern California DIY scene.
- F. None of above. chosen
Provenance (5 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_69c008dc56fc81908d43ffcc11d73bdd |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c06896d180819091548a728e903184 |
completed | March 22, 2026, 10:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6389bd9f48190af9811cf8cee124e |
completed | March 27, 2026, 7:58 a.m. |
| NEDg | Description generation | batch_69c63beaa5408190b4421f49634f3df1 |
completed | March 27, 2026, 8:12 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c63c5f7d508190bd263822cea1b782 |
completed | March 27, 2026, 8:14 a.m. |
Created at: March 22, 2026, 4:35 p.m.