product1: {{'asin': 'B0B5RG4BQQ', 'brand': 'Sywamol', 'bullet_point': '【 OUTDOOR TABLE】Our patio table provides plenty of room per person t the table, and ample leg space blow to stretch out, perfect to sit and eat at, put your drink on, or use a a table next to your grill, like you are at outdoor café.', 'item_name': 'Sywamol Patio DiningTable,Outdoor Garden Furniture Table Bar Table 43.3"x19.7"x41.3" Solid Acacia Wood', 'manufacturer': 'Sywamol', 'product_type': 'TABLE', 'item_height': '17.7 inches', 'item_length': '13.85 inches', 'item_width': '47.0 inches'}}
product2: {{'asin': 'B09YCZBVJW', 'brand': 'Sywamol', 'bullet_point': 'MODERN DESIGN: Fits great in any outdoor setting and matches other furniture with its simple and modern design', 'item_name': 'Sywamol Outdoor Patio,Bar Counter Table Backyard Furniture,Outdoor Bar Table for Patio, Balcony, Dinning Room, Bistro, Garden, 5 Piece Patio Dining Set Gray', 'manufacturer': 'Sywamol', 'product_type': 'TABLE', 'item_height': '29.0 inches', 'item_length': '39.0 inches', 'item_width': '78.0 inches'}}

You are given information of two TABLE products: product1 and product2.
product1 and product2 information consists of attributes and their values in JSON format.
I will ask you to compare attributes of these two products. Make your comparisons in the context of knowing that
these products are TABLE products, are listed in an E-Commerce Website and specifically in the context of that attribute each time.

Let's think step by step.
For each attribute ATTR_X in the product1 or product2 attributes:
1. Check whether the values of product1[ATTR_X] and product2[ATTR_X] are similar or different.
If product1[ATTR_X] and product2[ATTR_X] are numeric values, compare them for numerical equivalence and do not tolerate any decimal difference.
If product1[ATTR_X] and product2[ATTR_X] are textual values, compare them for semantic equivalence while allowing for minor syntactic differences.
Calculate a similarity score between product1[ATTR_X] and product2[ATTR_X] from 0.0 to 1.0. 
Similarity score must be close to 0.0 if product1[ATTR_X] and product2[ATTR_X] are syntactically and semantically very different, 
must be around 0.5 if there is good textual overlap but still different,
and must be close to 1.0 if they are semantically or numerically very similar. 
2. Output {{ATTR_X: {{"decision": "different" or "kind of similar" or "very similar", "explanation": explanation of decision, "similarity_score": similarity_score}} Wrap the entire output in JSON format.
user