=== Example 1 ===

"instruction": "Give a step-by-step analysis to determine if Entity 1 and Entity 2 refer to the same product, and make a matched or mismatched decision in the end. The answer is:",
"input": "Entity 1:{'title': 'microspot macdraft pe ( NAME_1 )', 'manufacturer': 'microspot ltd.', 'price': '99.95'} \n\nEntity 2:{'title': 'microspot macdraft pe personal edition', 'manufacturer': '', 'price': '85.99'}",
"prediction”: "mismatch",
"label": "match".

=== Example 2===
"instruction": "Give a step-by-step analysis to determine if Entity 1 and Entity 2 refer to the same product, and make a matched or mismatched decision in the end. The answer is:",
input": "Entity 1:{'title': 'peachtree by sage pro accounting 2007', 'manufacturer': 'sage software', 'price': '169.99'} \n\nEntity 2:{'title': 'peachtree ( r ) compatible continuous forms-accounting for windows 8.0-2004 version', 'manufacturer': '', 'price': '199.99'}"
"prediction": "match",
"label": "mismatch".


The flaws in the instruction that could lead to incorrect predictions in the given examples are:
The instruction asks for a step-by-step analysis, but it does not specify how the analysis should be done. This lack of guidance may result in different analyses being performed, leading to different predictions.
The instruction asks for a matched or mismatched decision in the end, but it does not specify any criteria for what constitutes a match or a mismatch. This lack of criteria may result in inconsistent predictions.
The instruction only provides two examples, which may not be representative of all possible cases. This may cause the model to make decisions based on incomplete or biased data.
The input data provided in the examples may not be sufficient to determine if the entities refer to the same product. For example, in Example 1, the price of Entity 1 is different from the price of Entity 2, but the instruction asks for a matched decision. This may cause the model to make an incorrect prediction.
The instruction does not specify how missing values (e.g., empty strings in Entity 2) should be handled. This may lead to different predictions depending on how the model chooses to handle these missing values.

Revise the instruction by referencing the flaws. Give two different versions of responses. Please separate the responses with 6 asterisk symbols: ******.
user