Triple
T2986422
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | General Electric CF34-10E |
E80635
|
entity |
| Predicate | noiseReductionFeature |
P44489
|
FINISHED |
| Object | low-noise fan design |
—
|
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: low-noise fan design | Statement: [General Electric CF34-10E, noiseReductionFeature, low-noise fan design]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: noiseReductionFeature Context triple: [General Electric CF34-10E, noiseReductionFeature, low-noise fan design]
-
A.
noiseReductionGoal
Indicates the intended target level or objective for reducing noise in a given context or system.
-
B.
noiseLevel
Indicates the intensity or amount of sound present in a given environment or from a specific source.
-
C.
usesEchoCancellation
Indicates that an entity employs echo cancellation techniques to reduce or eliminate echo in audio communication.
-
D.
usesCrosstalkCancellation
Indicates that one entity applies crosstalk cancellation techniques to reduce or eliminate interference between signals associated with another entity.
-
E.
hasNoiseTerm
Indicates that a given expression, model, or equation includes an additional noise term representing random or unexplained variation.
- 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_69ad8b16c3488190b47b6aa7a59a335b |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad99c76dfc8190b08bd6110ffabf25 |
completed | March 8, 2026, 3:46 p.m. |
| PD | Predicate disambiguation | batch_69ad9611fc348190a5d17d237f653f60 |
completed | March 8, 2026, 3:30 p.m. |
| PDg | Predicate description generation | batch_69ad97f5d28c8190899d90204dc43428 |
completed | March 8, 2026, 3:38 p.m. |
Created at: March 8, 2026, 2:59 p.m.