Traditionally, querying knowledge graphs is free of charge. However, ensuring data and service availability incurs costs to knowledge graphs providers. The Delayed-Answer Auction (DAA) model has been proposed to fund the maintenance of knowledge graph endpoints by allowing customers to sponsor entities in the Knowledge Graph so query results that include them are delivered in priority. However, implementing DAA with time-to-first results acceptable for data consumers is challenging because it requires reordering results according to bid values. In this paper, we present the AuctionKG approach to enable DAA with a low impact on query execution performance. AuctionKG relies on (i) reindexing sponsored entities by bid values to ensure they are processed first and (ii) Web preemption to ensure delayed answering. Experimental results demonstrate that our approach outperforms a baseline approach to enable DAA in terms of time for first results.