  The application of Graph Neural Networks (GNN) in track reconstruction is a
promising approach to cope with the challenges arising at the High-Luminosity
upgrade of the Large Hadron Collider (HL-LHC). GNNs show good track-finding
performance in high-multiplicity scenarios and are naturally parallelizable on
heterogeneous compute architectures.
  Typical high-energy-physics detectors have high resolution in the innermost
layers to support vertex reconstruction but lower resolution in the outer
parts. GNNs mainly rely on 3D space-point information, which can cause reduced
track-finding performance in the outer regions.
  In this contribution, we present a novel combination of GNN-based track
finding with the classical Combinatorial Kalman Filter (CKF) algorithm to
circumvent this issue: The GNN resolves the track candidates in the inner pixel
region, where 3D space points can represent measurements very well. These
candidates are then picked up by the CKF in the outer regions, where the CKF
performs well even for 1D measurements.
  Using the ACTS infrastructure, we present a proof of concept based on truth
tracking in the pixels as well as a dedicated GNN pipeline trained on
$t\bar{t}$ events with pile-up 200 in the OpenDataDetector.
