# Why Pixel RNN (Row LSTM) can capture triangular contexts?

I'm reading the paper Pixel Recurrent Neural Network. I have a question about Row LSTM. Why Row LSTM can capture triangular contexts?

In this paper,

the kernel of the one-dimensional convolution has size $$k \times 1$$ where $$k \geq 3$$; the larger value of $$k$$ the broader the context that is captured.

The one-dimensional kernel can capture only the left context. (Is this correct?)

The $$n \times n$$ kernel such as

$$\begin{bmatrix} 1 & 1 & 1 \\ 0 & 0 & 0 \\ 0 & 0 & 0 \end{bmatrix}$$

can capture triangular contexts.

Is this correct?