<｜begin▁of▁sentence｜>### Instruction:
Imagine you are analyzing the supply chain network of different ingredients required for a new food product development. The relationships between suppliers are mapped out as a network, where nodes represent suppliers, and edges represent the connections between them. The current network consists of connections [(1, 2), (1, 3), (2, 4), (3, 4), (4, 5)]. To optimize the supply chain and identify potential bottlenecks in the ingredient flow, could you utilize NetworkX to compute and list the betweenness centrality scores for each of these supplier connections? This metric will help understand the importance of each supplier's connection in the distribution of ingredients across the network.
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([(1, 2), (1, 3), (2, 4), (3, 4), (4, 5)])

# Calculate edge betweenness centrality
edge_betweenness = nx.edge_betweenness_centrality(G, normalized=True)

# Print the edge betweenness centrality
for edge, centrality in edge_betweenness.items():
    print(f'Edge {edge}: Betweenness Centrality {centrality}')
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