Task: Table-QA (source dataset: FinQA)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
| 2017                          | $ 200450   |
|:------------------------------|:-----------|
| 2018                          | 168926     |
| 2019                          | 136462     |
| 2020                          | 110063     |
| 2021                          | 82494      |
| thereafter                    | 486199     |
| future minimum lease payments | $ 1184594  |

Question:
in 2016 what was the percent of the total future minimum lease commitments due in 2019


pre_text: the fair value measurements of the borrowings under our credit agreement and receivables facility are classified as level 2 within the fair value hierarchy since they are determined based upon significant inputs observable in the market , including interest rates on recent financing transactions with similar terms and maturities .
we estimated the fair value by calculating the upfront cash payment a market participant would require at december 31 , 2016 to assume these obligations .
the fair value of our notes is classified as level 1 within the fair value hierarchy since it is determined based upon observable market inputs including quoted market prices in an active market .
the fair value of our euro notes is determined based upon observable market inputs including quoted market prices in a market that is not active , and therefore is classified as level 2 within the fair value hierarchy .
note 12 .
commitments and contingencies operating leases we are obligated under noncancelable operating leases for corporate office space , warehouse and distribution facilities , trucks and certain equipment .
the future minimum lease commitments under these leases at december 31 , 2016 are as follows ( in thousands ) : years ending december 31: .
post_text: rental expense for operating leases was approximately $ 211.5 million , $ 168.4 million and $ 148.5 million during the years ended december 31 , 2016 , 2015 and 2014 , respectively .
we guarantee the residual values of the majority of our truck and equipment operating leases .
the residual values decline over the lease terms to a defined percentage of original cost .
in the event the lessor does not realize the residual value when a piece of equipment is sold , we would be responsible for a portion of the shortfall .
similarly , if the lessor realizes more than the residual value when a piece of equipment is sold , we would be paid the amount realized over the residual value .
had we terminated all of our operating leases subject to these guarantees at december 31 , 2016 , our portion of the guaranteed residual value would have totaled approximately $ 59.0 million .
we have not recorded a liability for the guaranteed residual value of equipment under operating leases as the recovery on disposition of the equipment under the leases is expected to approximate the guaranteed residual value .
litigation and related contingencies we have certain contingencies resulting from litigation , claims and other commitments and are subject to a variety of environmental and pollution control laws and regulations incident to the ordinary course of business .
we currently expect that the resolution of such contingencies will not materially affect our financial position , results of operations or cash flows. .
label: 0.11520000000000001
qa: answer: 11.5%
ann_table_rows: 2
6
steps: op: divide2-1
arg1: 136462
arg2: 1184594
res: 11.5%
program: divide(136462, 1184594)
gold_inds: table_2: 2017 the 2019 of $ 200450 is 136462 ;
table_6: 2017 the future minimum lease payments of $ 200450 is $ 1184594 ;
exe_ans: 0.11520000000000001
tfidftopn: text_6: the future minimum lease commitments under these leases at december 31 , 2016 are as follows ( in thousands ) : years ending december 31: .
program_re: divide(136462, 1184594)
model_input: table_2
2017 the 2019 of $ 200450 is 136462 ;
table_6
2017 the future minimum lease payments of $ 200450 is $ 1184594 ;
text_7
rental expense for operating leases was approximately $ 211.5 million , $ 168.4 million and $ 148.5 million during the years ended december 31 , 2016 , 2015 and 2014 , respectively .