Unfortunately a lot of stuff going on in the RDF domain is behind firewalls so it's a bit hard to give a lot of details. But I can contribute some public and some private use-cases of where RDF is used:
Refinitiv (formerly Thomson Reuters Financial and Risk) knowledge graph is built completely on the RDF stack: https://www.refinitiv.com/en/products/knowledge-graph-feed
When I talked to them in late 2017, they told me they have 100 billion triples in their database, plus more in a versioning back-end. Their triplestore is open source: https://github.com/CM-Well/CM-Well
Several government-agencies all over the world start to build public RDF knowledge graphs. I'm closely involved in the one from the Swiss government, see my presentation from last week http://presentations.zazuko.com/Swiss-LOD-Platform/
There are similar projects in other countries like the Netherlands, Belgium, UK, etc. This stack makes a lot of sense for open data, as you can do some pretty crazy queries without spending 2 days on preparing your data. See for example the Swiss Open Data Advent Calendar of 2018: https://twitter.com/linkedktk/status/1076064066525949952
As I said there are many "behind the firewall" use-cases where people use the stack exactly because of its features like OWL. Yes it comes at a price (bootstrapping is not really super easy) but this is stuff we will still run in 40 years from now. I see it in:
Finance: Fraud detection, compliance, customer 360° views, ... Stardog (https://www.stardog.com/) lists Moody's, BNY Mellon and National Bank of Canada as customers, last week I've met someone from Credit Suisse which is Mr. RDF there.
* Production: You have a ton of databases containing products you create but there is no way to figure out what a final product consists of as the data is scattered across at least 5 of them. The automotive supplier I talk about here is using RDF to get that view.
Life sciences: The largest RDF dataset available to the public is UniProt and related datasets. In total they provide a SPARQL endpoint (RDF database) with 50 billion (!!) triples available. This is a highly popular dataset and is used in pretty much every larger pharmaceutical enterprise as well. See https://www.uniprot.org/ as a starting point. I know at least of one large life sciences company that just recently decided that RDF will be the base of all future data unification standards within the organization.
Insurance business: One of our customers is using RDF to unify a ton of different systems and get the 360° view as well about their customers.
RDF is an absolutely amazing stack and I do not see anything else available that gets remotely close to the power of it. The day I find something more powerful, I will be the first using it. But most of the time people dismissing RDF have zero clue about what it really can do.