In more "simple-minded" terms, you can think of your row reduced matrix as referring to
[tex]\begin{bmatrix}1 & 2 & 0 & 1 & 0 \\ 0 & 0 & 1 & -3 & 0 \\ 0 & 0 & 0 & 0 & 1 \\ 0 & 0 & 0 & 0 & 0 & 0\end{bmatrix}\begin{bmatrix}x_1 \\ x_2 \\ x_3 \\ x_4 \\ x_5 \\ x_6\end{bmatrix}= \begin{bmatrix}0 \\ 0 \\ 0 \\ 0\end{bmatrix}[/tex]
or simply the equations [itex]x_2+ 2x_2+ 0x_3+ x_4+ 0x_5= 0[/itex], [itex]0x_1+ 0x_2+ x_3- 3x_4+ 0x_5= 0[/itex], [itex]0x_1+ 0x_2+ 0x_3+ 0x_4+ x_5= 0[/itex], [itex]0x_1+ 0x_2+ 0x_3+ 0x_4+ 0x_5= 0[/itex].
If you interpret each of those equations as a "dot product" you can see how the vectors in the kernel must be "orthogonal" to the rows of the reduced matrix. Of course, you can also see, from the third equation that [itex]x_5[/itex] must be 0, from the second that [itex]x_3- 3x_4= 0[/itex] so that [itex]x_3= 3x_4[/itex], and from the first equation that [itex]x_1+ 2x_2+ x_4= 0[/itex] so that [itex]x_1= -2x_2- x_4[/itex]. That is, we can write
[tex]\begin{bmatrix}x_1 \\ x_2 \\ x_3 \\ x_4 \\ x_5\end{bmatrix}= \begin{bmatrix}-2x_2- x_4 \\ x_2 \\ 3x_4 \\ x_4 \\ 0\end{bmatrix}[/tex]
[tex]= \begin{bmatrix}-2x_2 \\ x_2 \\ 0 \\ 0 \\ 0\end{bmatrix}+ \begin{bmatrix}-x_4 \\ 0 \\ 3x_4 \\ x_4 \\ 0\end{bmatrix}[/tex]
[tex]= x_2\begin{bmatrix}- 2 \\ 1 \\ 0 \\ 0 \\ 0 \end{bmatrix}+ x_4\begin{bmatrix}-1 \\ 0 \\ 3 \\ 1 \\ 0 \end{bmatrix}[/tex]