Graph Neural Networks (GNNs) are increasingly deployed in graph-related applications, making ownership verification critical to protect their intellectual property against model theft. Fingerprinting and black-box watermarking are two main methods. However, the former relies on determining model similarity, which is computationally expensive and prone to ownership collisions after model post-processing such as model pruning or fine-tuning. The latter embeds backdoors, exposing watermarked models to the risk of backdoor attacks. Moreover, both methods enable ownership verification but do not convey additional information. As a result, each distributed model requires a unique trigger graph, and all trigger graphs must be used to query the suspect model during verification. Multiple queries increase the financial cost and the risk of detection.