  The goal of entity resolution is to identify records in multiple datasets
that represent the same real-world entity. However, comparing all records
across datasets can be computationally intensive, leading to long runtimes. To
reduce these runtimes, entity resolution pipelines are constructed of two
parts: a blocker that applies a computationally cheap method to select
candidate record pairs, and a matcher that afterwards identifies matching pairs
from this set using more expensive methods. This paper presents SC-Block, a
blocking method that utilizes supervised contrastive learning for positioning
records in the embedding space, and nearest neighbour search for candidate set
building. We benchmark SC-Block against eight state-of-the-art blocking
methods. In order to relate the training time of SC-Block to the reduction of
the overall runtime of the entity resolution pipeline, we combine SC-Block with
four matching methods into complete pipelines. For measuring the overall
runtime, we determine candidate sets with 99.5% pair completeness and pass them
to the matcher. The results show that SC-Block is able to create smaller
candidate sets and pipelines with SC-Block execute 1.5 to 2 times faster
compared to pipelines with other blockers, without sacrificing F1 score.
Blockers are often evaluated using relatively small datasets which might lead
to runtime effects resulting from a large vocabulary size being overlooked. In
order to measure runtimes in a more challenging setting, we introduce a new
benchmark dataset that requires large numbers of product offers to be blocked.
On this large-scale benchmark dataset, pipelines utilizing SC-Block and the
best-performing matcher execute 8 times faster than pipelines utilizing another
blocker with the same matcher reducing the runtime from 2.5 hours to 18
minutes, clearly compensating for the 5 minutes required for training SC-Block.
