HORNS1D^



= COW2C (6 tokens)
Translational equivalent: cow
by transcript \| by glosses \| by right neighbours \| by left neighbours
1248625-15324720-15465943 1248625-… \| 31-45f There are mostly cows in that region.
R	AREA1A^	PRIMARILY1	AREA1A^	COW2C*
L		PRIMARILY1		COW2C*
M	[MG]	[MG]		kuh kuh kuh kuh
1248625-15324720-15465943 1248625-… \| 31-45f If you ride your bike in the countryside, you can see cows everywhere.
R	COUNTRY1A	COW2C	BICYCLE1*	COW2C*	PRESENT-OR-HERE1*	COW2C	$INDEX1
L		COW2C	BICYCLE1*	COW2C*		COW2C
M	land	kuh	fahrrad	kuh		kuh
1248625-15324720-15465943 1248625-… \| 31-45f But mostly cows.
R			MOST1B	COW2C
L				COW2C
M			meist	kuh
1248625-15324720-15465943 1248625-… \| 31-45f If you ride your bike in the countryside, you can see cows everywhere.
R		$GEST-OFF1^*	COUNTRY1A	COW2C	BICYCLE1*	COW2C*	PRESENT-OR-HERE1*
L				COW2C	BICYCLE1*	COW2C*
M			land	kuh	fahrrad	kuh
1248625-15324720-15465943 1248625-… \| 31-45f Every once in a while, there might be a horse in the mix but it’s mostly cows.
R	$INDEX1	MOST1B	PRIMARILY1	COW2C	MUCH1C*	AREA1A^
L			PRIMARILY1	COW2C
M	pfer{d}	[MG]	[MG]	kuh	viel
1248625-15324720-15465943 1248625-… \| 31-45f If you ride your bike in the countryside, you can see cows everywhere.
R	BICYCLE1*	COW2C*	PRESENT-OR-HERE1*	COW2C	$INDEX1
L	BICYCLE1*	COW2C*		COW2C
M	fahrrad	kuh		kuh

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