Add explicit note on the mixed GNN + transformer content vs the graph-ml pipeline tag

README.md

@@ -38,6 +38,8 @@ All comparisons use the same honest protocol: temporal, case-level splits; model
 | `ft-bpi2018/` | EventTransformer (FT) | BPIC 2018 | **0.8718** | **0.5347** | +0.6pp acc / +3.3pp F1 over GNN |
 | `ft-bpi2015_1/` | EventTransformer (FT) | BPIC 2015 (municipality 1) | **0.4277** | **0.1863** | held-out log: **pure transfer**, +1.1pp acc over GNN |
 
+> **Note on the HF pipeline tag** — this repository spans two model families: the `gnn-*` checkpoints are graph neural networks over directly-follows graphs; the `ft-*` checkpoints are standard sequence transformers. `graph-ml` reflects the graph-based half and the project's process-mining roots — check which checkpoint you download (see the table above).
+
 Two inference scripts are provided: `inference.py` (GNN, requires `torch-geometric`) and `fm_inference.py` (transformer, requires only `torch` + `numpy`).
 
 ## Quickstart