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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?

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