Automated fact-checking pipelines typically begin with a filtering stage that decides which claims are worth verifying, given that the later evidence retrieval and verification components are expensive to apply at scale. A central task in this stage is verifiable claim detection, which asks whether a statement is in principle checkable against external evidence. Prior work on this task, as well as on the closely related notion of check-worthiness, conditions its decisions only on the claim sentence itself. We argue that this is restrictive, because deciding whether a statement is checkable often depends on identifying the entities and events it mentions, and on whether external information about them is actually available in the first place. Motivated by how downstream verification systems rely on retrieved evidence, we move retrieval upstream into the detection stage and introduce ContextClaim. Given an input claim, the approach identifies entity mentions, queries Wikipedia as a structured background source, and uses large language models to compress the retrieved material into short contextual summaries that are then passed to a classifier. Experiments are conducted on two domains and genres, namely the CheckThat! 2022 Twitter collection and the PoliClaim corpus of political debates, and cover both encoder and decoder only models under fine-tuning, zero-shot, and few-shot settings. The added context yields gains on verifiable claim detection in several configurations, although the size of the improvement varies with the dataset, the backbone model, and the training setup. We further find that the same retrieved summaries are useful beyond detection. Feeding them into a downstream verification model on FEVER improves verification F1. Component level analyses, human annotation, and error inspection further clarify the conditions under which retrieved context helps, and where it does not.

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