  Knowledge Graphs have been widely used to represent facts in a structured
format. Due to their large scale applications, knowledge graphs suffer from
being incomplete. The relation prediction task obtains knowledge graph
completion by assigning one or more possible relations to each pair of nodes.
In this work, we make use of the knowledge graph node names to fine-tune a
large language model for the relation prediction task. By utilizing the node
names only we enable our model to operate sufficiently in the inductive
settings. Our experiments show that we accomplish new scores on a widely used
knowledge graph benchmark.
