Triple
T3047545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | QuietTuning noise reduction |
E83485
|
entity |
| Predicate | marketedAs |
P1395
|
FINISHED |
| Object |
QuietTuning
QuietTuning is a branded automotive noise-reduction technology designed to make vehicle cabins quieter and more comfortable for occupants.
|
E323001
|
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: QuietTuning | Statement: [QuietTuning noise reduction, marketedAs, QuietTuning]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: QuietTuning Context triple: [QuietTuning noise reduction, marketedAs, QuietTuning]
-
A.
Tillotson
Tillotson is an English surname most notably associated with John Tillotson, a 17th-century Archbishop of Canterbury.
-
B.
Holley
Holley is a surname most notably associated with Alexander Lyman Holley, a prominent 19th-century American engineer and steel industry pioneer.
-
C.
Overtone
Overtone is a term commonly used in music and acoustics to refer to higher-frequency resonances that occur above a fundamental tone.
-
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.
TUNAIR
TUNAIR is the radio callsign used by Tunisair, the national flag carrier airline of Tunisia.
- 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: QuietTuning Triple: [QuietTuning noise reduction, marketedAs, QuietTuning]
Generated description
QuietTuning is a branded automotive noise-reduction technology designed to make vehicle cabins quieter and more comfortable for occupants.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: QuietTuning Target entity description: QuietTuning is a branded automotive noise-reduction technology designed to make vehicle cabins quieter and more comfortable for occupants.
-
A.
Tillotson
Tillotson is an English surname most notably associated with John Tillotson, a 17th-century Archbishop of Canterbury.
-
B.
Holley
Holley is a surname most notably associated with Alexander Lyman Holley, a prominent 19th-century American engineer and steel industry pioneer.
-
C.
Overtone
Overtone is a term commonly used in music and acoustics to refer to higher-frequency resonances that occur above a fundamental tone.
-
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.
TUNAIR
TUNAIR is the radio callsign used by Tunisair, the national flag carrier airline of Tunisia.
- 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_69ad8b24924c8190a9bb6f61d519e4ae |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9bad11b48190a5bd01e91a320e14 |
completed | March 8, 2026, 3:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1eef7d1e081908535b7d972a147eb |
completed | March 11, 2026, 10:38 p.m. |
| NEDg | Description generation | batch_69b1f0ad56dc81909c96018e34345fda |
completed | March 11, 2026, 10:46 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b1f13a34c88190aa829d8d87a5d29b |
completed | March 11, 2026, 10:48 p.m. |
Created at: March 8, 2026, 3:01 p.m.