from collections import defaultdict

class Graph:
    def __init__(self,V,edges=None,graph=None,directed=False,weighted=False,inf=float("inf")):
        self.V=V
        self.directed=directed
        self.weighted=weighted
        self.inf=inf
        if graph!=None:
            self.graph=graph
            """
            self.edges=[]
            for i in range(self.V):
                if self.weighted:
                    for j,d in self.graph[i]:
                        if self.directed or not self.directed and i<=j:
                            self.edges.append((i,j,d))
                else:
                    for j in self.graph[i]:
                        if self.directed or not self.directed and i<=j:
                            self.edges.append((i,j))
            """
        else:
            self.edges=edges
            self.graph=[[] for i in range(self.V)]
            if weighted:
                for i,j,d in self.edges:
                    self.graph[i].append((j,d))
                    if not self.directed:
                        self.graph[j].append((i,d))
            else:
                for i,j in self.edges:
                    self.graph[i].append(j)
                    if not self.directed:
                        self.graph[j].append(i)

    def SIV_DFS(self,s,bipartite_graph=False,cycle_detection=False,directed_acyclic=False,euler_tour=False,linked_components=False,lowlink=False,parents=False,postorder=False,preorder=False,subtree_size=False,topological_sort=False,unweighted_dist=False,weighted_dist=False):
        seen=[False]*self.V
        finished=[False]*self.V
        if directed_acyclic or cycle_detection or topological_sort:
            dag=True
        if euler_tour:
            et=[]
        if linked_components:
            lc=[]
        if lowlink:
            order=[None]*self.V
            ll=[None]*self.V
            idx=0
        if parents or cycle_detection or lowlink or subtree_size:
            ps=[None]*self.V
        if postorder or topological_sort:
            post=[]
        if preorder:
            pre=[]
        if subtree_size:
            ss=[1]*self.V
        if unweighted_dist or bipartite_graph:
            uwd=[self.inf]*self.V
            uwd[s]=0
        if weighted_dist:
            wd=[self.inf]*self.V
            wd[s]=0
        stack=[(s,0)] if self.weighted else [s]
        while stack:
            if self.weighted:
                x,d=stack.pop()
            else:
                x=stack.pop()
            if not seen[x]:
                seen[x]=True
                stack.append((x,d) if self.weighted else x)
                if euler_tour:
                    et.append(x)
                if linked_components:
                    lc.append(x)
                if lowlink:
                    order[x]=idx
                    ll[x]=idx
                    idx+=1
                if preorder:
                    pre.append(x)
                for y in self.graph[x]:
                    if self.weighted:
                        y,d=y
                    if not seen[y]:
                        stack.append((y,d) if self.weighted else y)
                        if parents or cycle_detection or lowlink or subtree_size:
                            ps[y]=x
                        if unweighted_dist or bipartite_graph:
                            uwd[y]=uwd[x]+1
                        if weighted_dist:
                            wd[y]=wd[x]+d
                    elif not finished[y]:
                        if (directed_acyclic or cycle_detection or topological_sort) and dag:
                            dag=False
                            if cycle_detection:
                                cd=(y,x)
            elif not finished[x]:
                finished[x]=True
                if euler_tour:
                    et.append(~x)
                if lowlink:
                    bl=True
                    for y in self.graph[x]:
                        if self.weighted:
                            y,d=y
                        if ps[x]==y and bl:
                            bl=False
                            continue
                        ll[x]=min(ll[x],order[y])
                    if x!=s:
                        ll[ps[x]]=min(ll[ps[x]],ll[x])
                if postorder or topological_sort:
                    post.append(x)
                if subtree_size:
                    for y in self.graph[x]:
                        if self.weighted:
                            y,d=y
                        if y==ps[x]:
                            continue
                        ss[x]+=ss[y]
        if bipartite_graph:
            bg=[[],[]]
            for tpl in self.edges:
                x,y=tpl[:2] if self.weighted else tpl
                if uwd[x]==self.inf or uwd[y]==self.inf:
                    continue
                if not uwd[x]%2^uwd[y]%2:
                    bg=False
                    break
            else:
                for x in range(self.V):
                    if uwd[x]==self.inf:
                        continue
                    bg[uwd[x]%2].append(x)
        retu=()
        if bipartite_graph:
            retu+=(bg,)
        if cycle_detection:
            if dag:
                cd=[]
            else:
                y,x=cd
                cd=self.Route_Restoration(y,x,ps)
            retu+=(cd,)
        if directed_acyclic:
            retu+=(dag,)
        if euler_tour:
            retu+=(et,)
        if linked_components:
            retu+=(lc,)
        if lowlink:
            retu=(ll,)
        if parents:
            retu+=(ps,)
        if postorder:
            retu+=(post,)
        if preorder:
            retu+=(pre,)
        if subtree_size:
            retu+=(ss,)
        if topological_sort:
            if dag:
                tp_sort=post[::-1]
            else:
                tp_sort=[]
            retu+=(tp_sort,)
        if unweighted_dist:
            retu+=(uwd,)
        if weighted_dist:
            retu+=(wd,)
        if len(retu)==1:
            retu=retu[0]
        return retu

    def Centroid(self,root=0):
        x=root
        parents,size=self.SIV_DFS(x,parents=True,subtree_size=True)
        while True:
            for y in self.graph[x]:
                if self.weighted:
                    y,d=y
                if y==parents[x]:
                    continue
                if size[y]*2>size[root]:
                    x=y
                    break
            else:
                for y in self.graph[x]:
                    if self.weighted:
                        y,d=y
                    if y==parents[x]:
                        continue
                    if size[root]<=2*size[y]:
                        return x,y
                return x,None

    def Centroid_Decomposition(self):
        E=self.edges
        P=[i for i in range(self.V)]
        prev_centroid=None
        stack=[(E,P,prev_centroid)]
        ans=0
        while stack:
            E,P,prev_centroid=stack.pop()
            if len(P)==1:
                centroid=P[0]
                continue
            G=Graph(len(P),edges=E,weighted=self.weighted)
            centroid,_=G.Centroid()
            parents,tour=G.SIV_DFS(centroid,parents=True,preorder=True)
            dp=[None]*len(P)
            dp_C=[None]*len(P)
            dp_C[centroid]=tuple()
            EE=[]
            PP=[]
            for i,x in enumerate(G.graph[centroid]):
                if G.weighted:
                    x,d=x
                dp[x]=(i,0)
                EE.append([])
                PP.append([P[x]])
                dp_C[x]=(d,)
            for x in tour[1:]:
                for y in G.graph[x]:
                    if G.weighted:
                        y,d=y
                    if y==parents[x]:
                        continue
                    i,j=dp[x]
                    jj=len(PP[i])
                    EE[i].append((j,jj,d))
                    PP[i].append(P[y])
                    dp[y]=(i,jj)
                    if len(dp_C[x])>=3 or d in dp_C[x]:
                        dp_C[y]=dp_C[x]
                    else:
                        dp_C[y]=dp_C[x]+(d,)
            tpls=[]
            dct=[[] for i in range(len(P))]
            for p,c in zip(dp,dp_C):
                if p==None:
                    continue
                dct[p[0]].append(c)
                tpls.append(c)
                if len(c)==2:
                    ans+=1
            ans+=cnt(tpls)
            for lst in dct:
                ans-=cnt(lst)
            centroid=P[centroid]
            for E,P in zip(EE,PP):
                stack.append((E,P,centroid))
        return ans

def cnt(dp_C):
    cnt1=defaultdict(int)
    cnt2=defaultdict(int)
    c1=0
    for tpl in dp_C:
        if len(tpl)==1:
            cnt1[tpl[0]]+=1
            c1+=1
        elif len(tpl)==2:
            a,b=tpl
            if a>b:
                a,b=b,a
            cnt2[(a,b)]+=1
    retu=0
    retu+=c1*(c1-1)//2
    for c in cnt1.values():
        retu-=c*(c-1)//2
    for (a,b),c in cnt2.items():
        retu+=c*(c-1)//2
        retu+=(cnt1[a]+cnt1[b])*c
    return retu

N,K=map(int,input().split())
edges=[]
for i in range(N-1):
    u,v,c=map(int,input().split())
    u-=1;v-=1
    edges.append((u,v,c))
G=Graph(N,edges=edges,weighted=True)
ans=G.Centroid_Decomposition()
print(ans)