Nvidia Tegra 4
E695739
Nvidia Tegra 4 is a mobile system-on-chip by Nvidia that significantly improved performance and power efficiency for smartphones and tablets over its predecessor.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Nvidia Tegra 4 canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T7857486 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nvidia Tegra 4 Context triple: [Nvidia Tegra 3, successor, Nvidia Tegra 4]
-
A.
Nvidia Tegra 3
Nvidia Tegra 3 is a quad-core ARM-based mobile system-on-chip designed by Nvidia for tablets and smartphones, known for integrating CPU, GPU, and memory controller to deliver improved performance and power efficiency.
-
B.
Nvidia Tegra X1
The Nvidia Tegra X1 is a mobile system-on-chip that combines ARM CPU cores with an integrated Maxwell-based GPU, widely known for powering devices like the Nintendo Switch.
-
C.
Snapdragon system-on-chip
The Snapdragon system-on-chip is a family of mobile processors widely used in smartphones and other devices, integrating CPU, GPU, modem, and other components to deliver high performance and power efficiency.
-
D.
A10 Fusion chip
The A10 Fusion chip is a 64‑bit ARM-based system-on-a-chip designed by Apple that significantly boosts performance and power efficiency for devices like the iPhone 7 and 7 Plus.
-
E.
Apple A4
Apple A4 is Apple’s first in-house designed system-on-a-chip, introduced in 2010 to power devices like the iPhone 4 and original iPad.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nvidia Tegra 4 Target entity description: Nvidia Tegra 4 is a mobile system-on-chip by Nvidia that significantly improved performance and power efficiency for smartphones and tablets over its predecessor.
-
A.
Nvidia Tegra 3
Nvidia Tegra 3 is a quad-core ARM-based mobile system-on-chip designed by Nvidia for tablets and smartphones, known for integrating CPU, GPU, and memory controller to deliver improved performance and power efficiency.
-
B.
Nvidia Tegra X1
The Nvidia Tegra X1 is a mobile system-on-chip that combines ARM CPU cores with an integrated Maxwell-based GPU, widely known for powering devices like the Nintendo Switch.
-
C.
Snapdragon system-on-chip
The Snapdragon system-on-chip is a family of mobile processors widely used in smartphones and other devices, integrating CPU, GPU, modem, and other components to deliver high performance and power efficiency.
-
D.
A10 Fusion chip
The A10 Fusion chip is a 64‑bit ARM-based system-on-a-chip designed by Apple that significantly boosts performance and power efficiency for devices like the iPhone 7 and 7 Plus.
-
E.
Apple A4
Apple A4 is Apple’s first in-house designed system-on-a-chip, introduced in 2010 to power devices like the iPhone 4 and original iPad.
- F. None of above. chosen
Statements (46)
| Predicate | Object |
|---|---|
| instanceOf | mobile system-on-chip ⓘ |
| announcedAtEvent | CES 2013 NERFINISHED ⓘ |
| announcementDate | 2013-01 ⓘ |
| applicationDomain |
mobile devices
ⓘ
smartphones ⓘ tablets ⓘ |
| codename | Wayne NERFINISHED ⓘ |
| CPUArchitecture | ARMv7-A NERFINISHED ⓘ |
| CPUCores | 4 ⓘ |
| CPUCoreType | ARM Cortex-A15 NERFINISHED ⓘ |
| family | Nvidia Tegra NERFINISHED ⓘ |
| generation | fourth generation Tegra SoC NERFINISHED ⓘ |
| GPU | Nvidia GeForce ULP NERFINISHED ⓘ |
| GPUCores | 72 ⓘ |
| hasCompanionCore | true ⓘ |
| hasFeature |
4-plus-1 CPU core design
ⓘ
computational photography enhancements ⓘ hardware-accelerated video decoding ⓘ hardware-accelerated video encoding ⓘ |
| improvementOver |
Nvidia Tegra 3 performance
ⓘ
Nvidia Tegra 3 power efficiency ⓘ |
| integrates |
image signal processor
ⓘ
video decoder ⓘ video encoder ⓘ |
| manufacturer | Nvidia NERFINISHED ⓘ |
| marketSegment | high-end mobile ⓘ |
| maxCPUFrequency | 1.9 GHz ⓘ |
| memoryTypeSupported |
DDR3L
ⓘ
LPDDR3 ⓘ |
| notableDevice |
Asus Transformer Pad TF701T
NERFINISHED
ⓘ
HP SlateBook x2 NERFINISHED ⓘ Nvidia Shield Portable NERFINISHED ⓘ Xiaomi Mi3 (Tegra 4 variant) NERFINISHED ⓘ |
| predecessor | Nvidia Tegra 3 NERFINISHED ⓘ |
| processNode | 28 nm ⓘ |
| processType | TSMC 28 nm HPL NERFINISHED ⓘ |
| successor | Nvidia Tegra K1 NERFINISHED ⓘ |
| supports |
4K video output
ⓘ
HDR photography ⓘ LTE (via external modem) ⓘ |
| supportsAPI |
CUDA (limited mobile profile)
NERFINISHED
ⓘ
Direct3D 9 NERFINISHED ⓘ OpenGL 4.x (desktop profile via compatibility) NERFINISHED ⓘ OpenGL ES 2.0 NERFINISHED ⓘ |
| targetOperatingSystem |
Android
NERFINISHED
ⓘ
Windows RT NERFINISHED ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Nvidia Tegra 4 Description of subject: Nvidia Tegra 4 is a mobile system-on-chip by Nvidia that significantly improved performance and power efficiency for smartphones and tablets over its predecessor.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.