thumb|Magomejuku 馬籠宿 aerial panorama thumb|250px|Hiroshige's print of Magome-juku, part of the Sixty-nine Stations of the Kiso Kaidō series was the forty-third of the sixty-nine stations of the Nakasendō connecting Edo with Kyoto in Edo period Japan. It is located in former Mino Province in what is now part of the city of Nakatsugawa, Gifu Prefecture, Japan. It was also the last of eleven stations along the Kisoji, which was the precursor to a part of the Nakasendō, running through the Kiso Valley.
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thumb|Magomejuku 馬籠宿 aerial panorama thumb|250px|Hiroshige's print of Magome-juku, part of the Sixty-nine Stations of the Kiso Kaidō series was the forty-third of the sixty-nine stations of the Nakasendō connecting Edo with Kyoto in Edo period Japan. It is located in former Mino Province in what is now part of the city of Nakatsugawa, Gifu Prefecture, Japan. It was also the last of eleven stations along the Kisoji, which was the precursor to a part of the Nakasendō, running through the Kiso Valley.
==History== thumb|150px|The mileage pole of Magome-juku around the street entrance. Magome-juku is located in a very mountainous section of the highway between Mino and Shinano Province. In places, the road was very steep and in the section between Magome-juku and the next station to the east, Tsumago-juku, travelers had to navigate an 800 meter mountain pass. The village is very old, and appears in Kamakura period records dated 1215 as part of the Tōyama Shōen, which had been awarded by Minamoto no Yoritomo to Kikuhime, the half-sister of Minamoto no Yoshinaka who lived in this location. In records dated 1487, it was referred to as "Kiso Magome" or "Ena-gun Magome", although Magoma was in Mino, and not in the Kiso River valley. In the early Edo period, the system of post stations on the Nakasendō was formalized by the Tokugawa shogunate in 1602, and it became a stopping place for traveling merchants () and it was also on the sankin-kōtai route used by various western daimyō to-and-from the Shogun's court in Edo.
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Discovered by embedding cosine similarity (sentence-transformers MiniLM, 384-dim).