We can rewrite the system of inequalities, including the restrictions on $A,B,C$ to be positive, as follows:
$$ \begin{align*}
A &\gt \frac{\nu g}{x} B + \frac{N g}{x} C \\
B &\gt \frac{\nu g}{y} A + \frac{N g}{y} C \\
C &\gt \frac{\nu g}{z} A + \frac{\nu g}{z} B \\ \\
A,B,C &\gt 0 \end{align*} $$
[Note that I've taken verbatim the terms as originally posted, but I asked for clarification about the third inequality in a Comment on the Question.]
The feasible region is convex, an intersection of six open half-spaces, each delineated by a plane passing through the origin. It is possible for this intersection to be empty, unlike the relaxed "weak inequalites" system where the origin $(0,0,0)$ will always be a solution.
Note that if $(A,B,C)$ is a feasible point, then so too is any positive scalar multiple $r\gt 0$ of it, $(rA,rB,rC)$. So geometrically, if the feasible region is not empty, it consists of a collection of open rays proceeding from the origin.
If the coefficients were known, we could represent the feasible region by taking its intersection with the plane:
$$ A + B + C = 1 $$
That intersection (if not empty) is an open polygon (the polygon excluding its perimeter). Such a polygon would be convex and have at most six sides, and its vertices can be readily computed by many linear programming packages. The feasible region of your problem is then all the open rays proceeding from the origin through a point in that open polygon. The points in the open polygon can be parameterized by barycentric coordinates.
If actual values of the coefficients can be given, I'd be happy to use them to illustrate this representation of solutions. Otherwise I could choose a few sets of coefficients to illustrate both the existence and non-existence of solutions.