import sys
readline=sys.stdin.readline
import heapq

class Path_Doubling:
    def __init__(self,N,permutation,lst=None,f=None,e=None):
        self.N=N
        self.permutation=permutation
        self.lst=lst
        self.f=f
        self.e=e

    def Build_Next(self,K=None):
        if K==None:
            K=self.N
        self.k=K.bit_length()
        self.permutation_doubling=[[None]*self.N for k in range(self.k)]
        for n in range(self.N):
            self.permutation_doubling[0][n]=self.permutation[n]
        if self.lst!=None:
            self.doubling=[[None]*self.N for k in range(self.k)]
            for n in range(self.N):
                self.doubling[0][n]=self.lst[n]
        for k in range(1,self.k):
            for n in range(self.N):
                if self.permutation_doubling[k-1][n]!=None:
                    self.permutation_doubling[k][n]=self.permutation_doubling[k-1][self.permutation_doubling[k-1][n]]
                    if self.f!=None:
                        self.doubling[k][n]=self.f(self.doubling[n][k-1],self.doubling[k-1][self.permutation_doubling[k-1][n]])

    def Permutation_Doubling(self,N,K):
        if K<0 or 1<<self.k<=K:
            return None
        for k in range(self.k):
            if K>>k&1 and N!=None:
                N=self.permutation_doubling[k][N]
        return N

    def Doubling(self,N,K):
        if K<0:
            return self.e
        retu=self.e
        for k in range(self.k):
            if K>>k&1:
                if self.permutation_doubling[k][N]==None:
                    return None
                retu=self.f(retu,self.doubling[k][N])
                N=self.permutation_doubling[k][N]
        return N,retu

    def Bisect(self,x,is_ok):
        if not is_ok(x):
            return -1,None
        K=0
        for k in range(self.k-1,-1,-1):
            if is_ok(self.permutation_doubling[k][x]):
                K|=1<<k
                x=self.permutation_doubling[k][x]
        return K,x

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 Build_LCA(self,s,segment_tree=False):
        self.lca_segment_tree=segment_tree
        if self.lca_segment_tree:
            self.lca_euler_tour,self.lca_parents,depth=self.SIV_DFS(s,euler_tour=True,parents=True,unweighted_dist=True)
            self.lca_dfs_in_index=[None]*self.V
            self.lca_dfs_out_index=[None]*self.V
            for i,x in enumerate(self.lca_euler_tour):
                if x>=0:
                    self.lca_dfs_in_index[x]=i
                else:
                    self.lca_dfs_out_index[~x]=i
            self.ST=Segment_Tree(2*self.V,lambda x,y:min(x,y),self.V)
            lst=[None]*(2*self.V)
            for i in range(2*self.V-1):
                if self.lca_euler_tour[i]>=0:
                    lst[i]=depth[self.lca_euler_tour[i]]
                else:
                    lst[i]=depth[self.lca_parents[~self.lca_euler_tour[i]]]
            lst[2*self.V-1]=-1
            self.ST.Build(lst)
        else:
            self.lca_parents,self.lca_depth=self.SIV_DFS(s,parents=True,unweighted_dist=True)
            self.lca_PD=Path_Doubling(self.V,self.lca_parents)
            self.lca_PD.Build_Next(self.V)

    def LCA(self,a,b):
        if self.lca_segment_tree:
            m=min(self.lca_dfs_in_index[a],self.lca_dfs_in_index[b])
            M=max(self.lca_dfs_in_index[a],self.lca_dfs_in_index[b])
            x=self.lca_euler_tour[self.ST.Fold_Index(m,M+1)]
            if x>=0:
                lca=x
            else:
                lca=self.lca_parents[~x]
        else:
            if self.lca_depth[a]>self.lca_depth[b]:
                a,b=b,a
            b=self.lca_PD.Permutation_Doubling(b,self.lca_depth[b]-self.lca_depth[a])
            if a!=b:
                for k in range(self.lca_PD.k-1,-1,-1):
                    if self.lca_PD.permutation_doubling[k][a]!=self.lca_PD.permutation_doubling[k][b]:
                        a,b=self.lca_PD.permutation_doubling[k][a],self.lca_PD.permutation_doubling[k][b]
                a,b=self.lca_PD.permutation_doubling[0][a],self.lca_PD.permutation_doubling[0][b]
            lca=a
        return lca

    def Dijkstra(self,s,route_restoration=False):
        dist=[self.inf]*self.V
        dist[s]=0
        queue=[(0,s)]
        if route_restoration:
            parents=[None]*self.V
        while queue:
            dx,x=heapq.heappop(queue)
            if dist[x]<dx:
                continue
            for y,dy in self.graph[x]:
                if dist[y]>dx+dy:
                    dist[y]=dx+dy
                    if route_restoration:
                        parents[y]=x
                    heapq.heappush(queue,(dist[y],y))
        if route_restoration:
            return dist,parents
        else:
            return dist

N,K=map(int,readline().split())
graph=[[] for x in range(N+K)]
for _ in range(N-1):
    A,B,C=map(int,readline().split())
    A-=1;B-=1
    graph[A].append((B,2*C))
    graph[B].append((A,2*C))
G=Graph(N+K,graph=graph,directed=True,weighted=True)
dist_T=G.SIV_DFS(0,weighted_dist=True)
G.Build_LCA(0)
for k in range(N,N+K):
    M,P=map(int,readline().split())
    X=list(map(int,readline().split()))
    for m in range(M):
        X[m]-=1
        graph[X[m]].append((k,P))
        graph[k].append((X[m],P))
dist=[G.Dijkstra(k) for k in range(N,N+K)]
Q=int(readline())
for q in range(Q):
    U,V=map(int,readline().split())
    U-=1;V-=1
    lca=G.LCA(U,V)
    ans=dist_T[U]+dist_T[V]-2*dist_T[lca]
    if K:
        ans=min(ans,min(dist[k][V]+dist[k][U] for k in range(K)))
    ans//=2
    print(ans)