#頂点数と辺数を取得
vertex_number, edge_number = map(int, input().split())

#隣接行列を作成 (辺がないところは 0，便宜上，(n+1)×(n+1)行列をつくっている)

A = [[0 for i in range(vertex_number+1)] for j in range(vertex_number+1)] #内包表記にしないと，一個の変更で複数のリストが変更されてしまうので注意
for i in range(edge_number):
    j,k,d = map(int, input().split())
    A[j][k] = d
    A[k][j] = d
    

#T_max と info を取得
T_max = int(input())
info = []

for i in range(T_max):
    x = int(input())
    if x == 0:
        info.append([x])
    else:
        y,z = map(int, input().split())
        info.append([x,y,z])
''''
#info から　info_vertex を作成
for i in range(len(Req)): #Request から　Visit を作成 
    if Req[i][2] not in Visit:
        Visit.append(Req[i][2])

#フロイド・ワーシャル法
D = [[0 for i in range(vertex_number+1)] for j in range(vertex_number+1)] #最短距離行列
Next = [[0 for i in range(vertex_number+1)] for j in range(vertex_number+1)] #最短経路行列
#最短距離行列の初期化
for i in range(vertex_number+1):
    for j in range(vertex_number+1):
        if (i != 0) and (j != 0): #iが0 または j が0のときは何もしない
            if i == j:             # i=j のときは 0
                D[i][j] = 0
            elif A[i][j] == 0:      #辺がないところは距離無限大
                D[i][j] = 100000
            else: D[i][j] = A[i][j] #辺があるところはその辺の長さ

#最短経路行列の初期化
for i in range(vertex_number+1):
    for j in range(vertex_number+1):
        if (i != 0) and (j != 0) and (i != j): 
            Next[i][j] = j
#最短距離行列の計算
for k in range(vertex_number+1):
    for i in range(vertex_number+1):
        for j in range(vertex_number+1):
            if (k != 0) and (i != 0) and (j != 0):
                if D[i][j] > D[i][k]+D[k][j]:
                    D[i][j] = D[i][k]+D[k][j]
                    Next[i][j] = Next[i][k]
                    
                    
                    
                    

def Shortest_path(i,j): #iからj への最短経路をリストとして出力
    SP = [i]
    x = Next[i][j]
    while x != j:
        SP.append(x)
        x = Next[x][j]
    SP.append(x)
    return SP


def Request(a,b): #時刻 a< t<=b の間に発生したリクエスト　(注文個数と注文IDと頂点)のリストのリストを出力
    R = []
    if a == b: #a=b ならば [] を出力
        return R
    else:
        for i in range(b-a):
            if info[i+a+1] != [0]:
                R.append(info[i+a+1])
        return R


#Algorithm (貪欲法)

t = 0
t_start = 0
Root = []

while t < T_max-1:
    Req = []
    Visit = []
    if Request(t_start,t) == []:
        counter = 0
        while (info[t] == [0]) and (t != T_max - 1):
            t += 1
            counter += 1
            Root.append(-1)
        Req.append(info[t])
        t_start = t
    else:
        Req = Request(t_start,t)
        t_start = t
        
    if Req != [0]:
        
        for i in range(len(Req)): #Request から　Visit を作成 
            if Req[i][2] not in Visit:
                Visit.append(Req[i][2])
                
        t_path = 0       
        active_path = [1]
        real_path = []
        while Visit != []:
            x = 0
            u = 0
            for i in range(len(Visit)):
                if i == 0:
                    x = D[active_path[-1]][Visit[0]]
                    u = Visit[0]
                elif x > D[active_path[-1]][Visit[i]]:
                    x = D[active_path[-1]][Visit[i]]
                    u = Visit[i]
            Visit.remove(u)
            active_path.append(u)
            t_path += x
        t_path += D[active_path[-1]][1]
        active_path.append(1)
        t += t_path

        #active_pathからRootを作成
        for i in range(len(active_path)):
            if i == 0:
                real_path =real_path + Shortest_path(active_path[i],active_path[i+1])
            elif i != len(active_path)-1:
                real_path.pop(-1)
                real_path =real_path + Shortest_path(active_path[i],active_path[i+1])
        for j in range(len(real_path)):
            if j < len(real_path)-1:
                for k in range(A[real_path[j]][real_path[j+1]]):
                    Root.append(real_path[j+1])

#Root から最終出力を生成

Time = 0

for i in range(T_max):
    if i < len(Root):
        print(Root[i])
    else:
        print(-1)
''''
for i in range(T_max):
    print(-1)

### FIXED CODE ###

#頂点数と辺数を取得
vertex_number, edge_number = map(int, input().split())

#隣接行列を作成 (辺がないところは 0，便宜上，(n+1)×(n+1)行列をつくっている)

A = [[0 for i in range(vertex_number+1)] for j in range(vertex_number+1)] #内包表記にしないと，一個の変更で複数のリストが変更されてしまうので注意
for i in range(edge_number):
    j,k,d = map(int, input().split())
    A[j][k] = d
    A[k][j] = d
    

#T_max と info を取得
T_max = int(input())
info = []

for i in range(T_max):
    x = int(input())
    if x == 0:
        info.append([x])
    else:
        y,z = map(int, input().split())
        info.append([x,y,z])


#フロイド・ワーシャル法
D = [[0 for i in range(vertex_number+1)] for j in range(vertex_number+1)] #最短距離行列
Next = [[0 for i in range(vertex_number+1)] for j in range(vertex_number+1)] #最短経路行列
#最短距離行列の初期化
for i in range(vertex_number+1):
    for j in range(vertex_number+1):
        if (i != 0) and (j != 0): #iが0 または j が0のときは何もしない
            if i == j:             # i=j のときは 0
                D[i][j] = 0
            elif A[i][j] == 0:      #辺がないところは距離無限大
                D[i][j] = 100000
            else: D[i][j] = A[i][j] #辺があるところはその辺の長さ

#最短経路行列の初期化
for i in range(vertex_number+1):
    for j in range(vertex_number+1):
        if (i != 0) and (j != 0) and (i != j): 
            Next[i][j] = j
#最短距離行列の計算
for k in range(vertex_number+1):
    for i in range(vertex_number+1):
        for j in range(vertex_number+1):
            if (k != 0) and (i != 0) and (j != 0):
                if D[i][j] > D[i][k]+D[k][j]:
                    D[i][j] = D[i][k]+D[k][j]
                    Next[i][j] = Next[i][k]
                    
                    
                    
                    

def Shortest_path(i,j): #iからj への最短経路をリストとして出力
    SP = [i]
    x = Next[i][j]
    while x != j:
        SP.append(x)
        x = Next[x][j]
    SP.append(x)
    return SP

'''
def Request(a,b): #時刻 a< t<=b の間に発生したリクエスト　(注文個数と注文IDと頂点)のリストのリストを出力
    R = []
    if a == b: #a=b ならば [] を出力
        return R
    else:
        for i in range(b-a):
            if info[i+a+1] != [0]:
                R.append(info[i+a+1])
        return R
'''
def Request(a,b): #時刻 a< t<=b の間に発生したリクエストの頂点をかぶりをなくして出力
    R = []
    if a == b: #a=b ならば [] を出力
        return R
    else:
        for i in range(b-a):
            if info[i+a+1] != [0]:
                if info[i+a+1][2] not in R:
                    R.append(info[i+a+1][2])
        return R

#Algorithm (貪欲法)

t = 0
t_start = 0
Root = []

while t < T_max-1:
    Req = [] #おとづれたいactive vertex set
    if Request(t_start,t) == []: # いまもっているおとづれるべき頂点集合が空ならばリクエストが来るまで待つ
        while (info[t] == [0]) and (t != T_max - 1):
            t += 1
            Root.append(-1)
        if t != T_max - 1:
            Req.append(info[t][2])

    else:
        Req = Request(t_start,t)
        
    t_start = t
        
    if t != T_max - 1:
        
        t_path = 0       
        active_path = [1]
        real_path = []
        while Req != []:
            x = 0
            u = 0
            for i in range(len(Req)):
                if i == 0:
                    x = D[active_path[-1]][Req[0]]
                    u = Req[0]
                elif x > D[active_path[-1]][Req[i]]:
                    x = D[active_path[-1]][Req[i]]
                    u = Req[i]
            Req.remove(u)
            active_path.append(u)
            t_path += x
        t_path += D[active_path[-1]][1]
        active_path.append(1)
        t += t_path

        #active_pathからRootを作成
        for i in range(len(active_path)):
            if i == 0:
                real_path =real_path + Shortest_path(active_path[i],active_path[i+1])
            elif i != len(active_path)-1:
                real_path.pop(-1)
                real_path =real_path + Shortest_path(active_path[i],active_path[i+1])
        for j in range(len(real_path)):
            if j < len(real_path)-1:
                for k in range(A[real_path[j]][real_path[j+1]]):
                    Root.append(real_path[j+1])

#Root から最終出力を生成

for i in range(T_max):
    if i < len(Root):
        print(Root[i])
    else:
        print(-1)
'''
for i in range(T_max):
    print(-1)
'''