Transaction Details:

Hash 0f1e855d31ae295d52c8b431ee3beedb7fcaed671553e0aa56c54ea00a3cda1f
Blockhash 25e2753da91ee0dcfcc32e0821c0ba19c81defac78fcfe81da20b19aa9b160b3
Blocktime 2019-12-03 07:51
Confirmations 262549

Inputs

Index Previous Output Address
0 569e275a49b9911cce405bc046e2f75ab54132e9a54c2964165b5476f3b65bd6:1 b'2QTa5N4aeSrCB2MLoPdPF4AsTjstJgcLTnd'

Outputs

Index Redeemed in Address Amount
0 Not yet redeemed N/A 0 CBL
1 faf3b04faeb7889a0d64b14d79b8c8d0e8a721f9a129c4d34aaa037a7d140061 2QTa5N4aeSrCB2MLoPdPF4AsTjstJgcLTnd 100.00567821 CBL
{
"txid": 0f1e855d31ae295d52c8b431ee3beedb7fcaed671553e0aa56c54ea00a3cda1f,
"time": 1575359512,
"blocktime": 1575359512,
"blockhash": 25e2753da91ee0dcfcc32e0821c0ba19c81defac78fcfe81da20b19aa9b160b3,
"confirmations": 262549,
"vin ": [
{
"txid": 569e275a49b9911cce405bc046e2f75ab54132e9a54c2964165b5476f3b65bd6
"vout": 1
"scriptSig": {'asm': '3045022100a78a883f3b3fdfa65102c7a00bd749f80ed9bf16750a6a4a84ca17a70721275b02206a937218c7ab0f3834061220e6455c6471c2f953c0a6d99f40e695dcf189066d01 03f4157f5bbcf4594cb612f878fa622f4fa99713ebd638179751679d259fb49e38', 'hex': '483045022100a78a883f3b3fdfa65102c7a00bd749f80ed9bf16750a6a4a84ca17a70721275b02206a937218c7ab0f3834061220e6455c6471c2f953c0a6d99f40e695dcf189066d012103f4157f5bbcf4594cb612f878fa622f4fa99713ebd638179751679d259fb49e38'}
"sequence": 4294967295
}
],
"vout ": [
{
"value": 0
"n": 0
"scriptPubKey": {
"asm": 73706b65164ff70a0a0153ccf31bbaf3f020c19d OP_DROP 73706b6b434345664d4344485962516361 OP_DROP 73706b6b4354454766703574724a564b69 OP_DROP 73706b6b4344584e4a4878546235715554 OP_DROP 73706b6b4352575134356a3377586e6f51 OP_DROP 73706b6602 OP_DROP OP_RETURN 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
"hex": 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
"type": nulldata
},
"items": [
{'type': 'stream', 'name': 'iscc', 'createtxid': '9dc120f0f3ba1bf3cc53010a0af74f164961f37595bdd9a89b5064e60304c070', 'streamref': '22580-300-49565', 'publishers': ['2QTa5N4aeSrCB2MLoPdPF4AsTjstJgcLTnd'], 'keys': ['CCEfMCDHYbQca', 'CTEGfp5trJVKi', 'CDXNJHxTb5qUT', 'CRWQ45j3wXnoQ'], 'offchain': False, 'data': {'json': {'title': 'metric learning a support vector approach', 'tophash': 'b2d92e22b3fa40c5f14ec26e57f0a62fdee795c34ea8aa2b66eb9a037adea03d', 'meta': [{'schema': 'schema.org', 'mediatype': 'application/ld+json', 'data': {'@context': 'http://schema.org/', '@type': 'ScholarlyArticle', 'datePublished': None, 'encoding': [{'@type': 'MediaObject', 'contentUrl': 'https://link.springer.com/content/pdf/10.1007%2F978-3-540-87481-2_9.pdf', 'encodingFormat': 'application/pdf'}], 'genre': 'book-chapter', 'identifier': [{'@type': 'PropertyValue', 'propertyID': 'DOI', 'value': '10.1007/978-3-540-87481-2_9'}, {'@type': 'PropertyValue', 'propertyID': 'ISCC', 'value': 'CCEfMCDHYbQca-CTEGfp5trJVKi-CDXNJHxTb5qUT-CRWQ45j3wXnoQ'}], 'name': 'Metric Learning: A Support Vector Approach', 'publisher': {'@type': 'Organization', 'name': 'Springer Berlin Heidelberg'}, 'author': [{'@type': 'Person', 'name': 'Nam Nguyen'}, {'@type': 'Person', 'name': 'Yunsong Guo'}]}}]}}}
]
},
{
"value": 100.00567821
"n": 1
"scriptPubKey": {
"asm": OP_DUP OP_HASH160 70f3924b82ad8ba9ca4b38ee85a83bd99c142f9f OP_EQUALVERIFY OP_CHECKSIG
"hex": 76a91470f3924b82ad8ba9ca4b38ee85a83bd99c142f9f88ac
"reqSigs": 1
"type": pubkeyhash
"addresses": ['2QTa5N4aeSrCB2MLoPdPF4AsTjstJgcLTnd']
}
}
]
}