<｜begin▁of▁sentence｜>### Instruction:
As a personal chef, imagine you are tasked with crafting the perfect menu for an event, ensuring the optimal combination of dishes that complement each other while keeping within a specified budget. Now, each of the dishes has a certain compatibility weight with others, as well as cost implications.

Consider a set of potential dishes symbolized by the ingredients list. We have an array of pairings and their corresponding affinities, much like a recipe card. These pairings go like [(1, 2, {'weight': 3}), (1, 3, {'weight': 1}), (2, 3, {'weight': 4}), (2, 4, {'weight': 2}), (3, 4, {'weight': 5})]. Your challenge is to determine a combination of these dishes that would result in the most harmonious menu - essentially creating a 'minimum spanning tree' of flavors - by utilizing the least costly paths of compatibility without compromising on the overall experience. This methodical approach is akin to employing Kruskal's algorithm in your kitchen setting.

Could you tell me which dish pairings would make it onto this cost-effective yet savory menu?
### Response:
import networkx as nx
from networkx.algorithms import tree

# Create a weighted graph
G = nx.Graph()
G.add_edges_from([(1, 2, {'weight': 3}), (1, 3, {'weight': 1}), (2, 3, {'weight': 4}), (2, 4, {'weight': 2}), (3, 4, {'weight': 5})])

# Find minimum spanning edges by Kruskal's algorithm
mst = tree.minimum_spanning_edges(G, algorithm="kruskal", data=False)
edgelist = list(mst)

# Display the edges in the minimum spanning tree
print(sorted(sorted(e) for e in edgelist))
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