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### Instruction:
Extract the cause and effect entities from the following sentence:
The RTGS account holder whose account is subject to the debit is not blocked
### Assistant:
EFFECT_1: is not blocked
VARIABLE: account
KEYWORD: whose
CONDITION: blocked
CAUSE_1: account is subject to the debit
NEGATION: is not
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
For a EURO-CB, this check is not relevant since a EURO-CB Account can be negative
### Assistant:
VARIABLE: a EURO-CB
CONDITION: can be negative
KEYWORD: For
CAUSE_1: a EURO-CB
EFFECT_1: this check is not relevant
CAUSE_2: a EURO-CB Account can be negative
NEGATION: is not
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Where no bilateral Limit is defined, RTGS shall check the multilateral Limit
### Assistant:
KEYWORD: Where
NEGATION: no
VARIABLE: RTGS
CONDITION: shall check the multilateral Limit
CAUSE_1: no bilateral Limit is defined
EFFECT_1: RTGS shall check the multilateral Limit
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Where not enough liquidity is available, RTGS shall queue the payment order
### Assistant:
KEYWORD: Where
NEGATION: not
VARIABLE: RTGS
CONDITION: shall queue the payment order
CAUSE_1: not enough liquidity is available
EFFECT_1: RTGS shall queue the payment order
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The re-ordering of the queued payment orders possibly triggers the settlement of another payment order
### Assistant:
VARIABLE: another payment order
CONDITION: the settlement of
CAUSE_1: The re-ordering of the queued payment orders
KEYWORD: possibly triggers
EFFECT_1: the settlement of another payment order
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
Where several payment orders were selected they will be put at the bottom of the queue according to their previous order
### Assistant:
KEYWORD: Where
VARIABLE: they
CONDITION: will be put at the bottom of the queue according to their previous order
CAUSE_1: several payment orders were selected
EFFECT_1: they will be put at the bottom of the queue according to their previous order
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If the message content is either invalid or would result in reference data checks to fail, it will be rejected and a rejection notification with the appropriate error code(s) will be sent to the sender of the revocation/recall
### Assistant:
KEYWORD: If
VARIABLE: a rejection notification with the appropriate error code(s)
CONDITION: will be sent to the sender of the revocation/recall
OR: or
CAUSE_1: invalid
CAUSE_2: would result in reference data checks to fail
AND: and
EFFECT_1: it will be rejected
EFFECT_2: a rejection notification with the appropriate error code(s) will be sent to the sender of the revocation/recall
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If the message content is valid and reference data checks have been passed successfully, RTGS will perform a revocation attempt of the original payment order the revoke&recall request message is referring to
### Assistant:
KEYWORD: If
VARIABLE: RTGS
CONDITION: will perform a revocation attempt of the original payment order the revoke&recall request message is referring to
AND: and
CAUSE_1: the message content is valid
CAUSE_2: reference data checks have been passed successfully
EFFECT_1: RTGS will perform a revocation attempt of the original payment order the revoke&recall request message is referring to
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If the original payment order has a negative final status (rejected or revoked) a denial notification with appropriate error code is sent to the sender of the revoke & recall request
### Assistant:
KEYWORD: the original payment order
CONDITION: is sent to the sender of the revoke & recall request
VARIABLE: a denial notification with appropriate error code
CAUSE_1: the original payment order has a negative final status
EFFECT_1: a denial notification with appropriate error code is sent to the sender of the revoke & recall request
NOT_RELEVANT: (rejected or revoked)
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If the original payment order has a positive final status (settled) or the original payment order cannot be found in the list of payments of the current business day, the revoke&recall request is forwarded to the payment receiver quoted in the request (except it the original payment order is a direct debit or payment return )
### Assistant:
KEYWORD: it
VARIABLE: the original payment order
CONDITION: is a direct debit or payment return
OR: or
NEGATION: except
CAUSE_1: the original payment order has a positive final status
CAUSE_2: the original payment order cannot be found in the list of payments of the current business day
EFFECT_1: the revoke&recall request is forwarded to the payment receiver quoted in the request
CAUSE_3: the original payment order is a direct debit or payment return
NOT_RELEVANT: (settled)
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
For direct debits, the creditor (=sender) can initiate the revocation
### Assistant:
VARIABLE: the creditor
CONDITION: can initiate the revocation
KEYWORD: For
CAUSE_1: direct debits
EFFECT_1: can initiate the revocation
NOT_RELEVANT: (=sender)
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Payment orders which are already settled cannot be revoked anymore
### Assistant:
VARIABLE: Payment orders
KEYWORD: which
CONDITION: be revoked anymore
NEGATION: cannot
CAUSE_1: are already settled
EFFECT_1: cannot be revoked anymore
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
This check is not performed if the debitor or the creditor is a CB Accounts
### Assistant:
VARIABLE: the creditor
NEGATION: is not
CONDITION: is a CB Accounts
KEYWORD: if
OR: or
EFFECT_1: This check is not performed
CAUSE_1: the debitor
CAUSE_2: the creditor is a CB Accounts
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Regular liquidity transfer orders (e.g. from RTGS DCA to sub account) at these business events can be set up through standing orders
### Assistant:
VARIABLE: at these business events
CONDITION: can be set up
KEYWORD: through
CAUSE_1: standing orders
EFFECT_1: at these business events can be set up
NOT_RELEVANT: (e.g. from RTGS DCA to sub account)
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Whenever the AS using this interfaced procedure starts a cycle, the liquidity on the sub account involved will be controlled by the AS
### Assistant:
KEYWORD: Whenever
VARIABLE: the liquidity on the sub account involved
CONDITION: will be controlled by the AS
CAUSE_1: the AS using this interfaced procedure
CAUSE_2: starts a cycle
EFFECT_1: the liquidity on the sub account involved will be controlled by the AS
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
During a running cycle, a liquidity transfer which increases the balance on the sub-account settles immediately
### Assistant:
KEYWORD: During
VARIABLE: cycle
CONDITION: settles immediately
CAUSE_1: a running cycle
CAUSE_2: increases the balance on the sub-account
EFFECT_1: settles immediately
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
The control is given back to the settlement bank through the end of cycle
### Assistant:
VARIABLE: cycle
CONDITION: the end of
KEYWORD: through
EFFECT_1: The control is given back to the settlement bank
CAUSE_1: the end of cycle
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Payment orders stemming from batch procedures of AS are excluded
### Assistant:
VARIABLE: Payment orders
CONDITION: are excluded
CAUSE_1: Payment orders stemming from batch procedures of AS
EFFECT_1: are excluded
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
All described queries in this section shall be provided in U2A and A2A mode unless otherwise stated
### Assistant:
NEGATION: unless
CONDITION: A2A mode
CAUSE_1: otherwise stated
VARIABLE: All described queries in this section
AND: and
EFFECT_1: All described queries in this section shall be provided in U2A
EFFECT_2: A2A mode
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If the credit line modification order is valid, the credit line update shall be executed immediately
### Assistant:
KEYWORD: If
VARIABLE: the credit line update
CONDITION: shall be executed immediately
CAUSE_1: the credit line modification order is valid
EFFECT_1: the credit line update shall be executed immediately
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
As of ECMS launch, there is a need to do a value date check
### Assistant:
VARIABLE: ECMS launch
CONDITION: is a need to do a value date check
EFFECT_1: there is a need to do a value date check
CAUSE_1: ECMS launch
KEYWORD: As of
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If the update of the credit line is not possible, the request shall be queued
### Assistant:
KEYWORD: If
VARIABLE: the request
NEGATION: is not
CONDITION: shall be queued
CAUSE_1: the update of the credit line is not possible
EFFECT_1: the request shall be queued
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If the update of the credit line is not possible, CLM shall queue the credit line modification request
### Assistant:
KEYWORD: If
VARIABLE: CLM
NEGATION: is not
CONDITION: shall queue the credit line modification request
CAUSE_1: the update of the credit line is not possible
EFFECT_1: CLM shall queue the credit line modification request
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Connected payment orders cannot be revoked as there is no queuing
### Assistant:
KEYWORD: as
VARIABLE: Connected payment orders
CONDITION: be revoked
NEGATION: is no
EFFECT_1: Connected payment orders cannot be revoked
CAUSE_1: there is no queuing
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
CLM account holders can use the deposit facility to make overnight deposits with their national CBs
### Assistant:
VARIABLE: overnight deposits with their national CBs
CONDITION: to make
CAUSE_1: CLM account holders can use the deposit facility
EFFECT_1: to make overnight deposits with their national CBs
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Once processed, CLM shall send an acknowledgement to CBS
### Assistant:
KEYWORD: Once
CONDITION: shall send an acknowledgement to CBS
VARIABLE: CLM
CAUSE_1: processed
EFFECT_1: CLM shall send an acknowledgement to CBS
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Once processed, CLM send an acknowledgement to CBS
### Assistant:
KEYWORD: Once
CAUSE_1: processed
CONDITION: send an acknowledgement to CBS
VARIABLE: CLM
EFFECT_1: CLM send an acknowledgement to CBS
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Day balances are available before initiating the process
### Assistant:
VARIABLE: Day balances
CONDITION: are available
CAUSE_1: initiating the process
EFFECT_1: Day balances are available
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The interest payment order shall debit the CLM account holder’s MCA and credit the CB account
### Assistant:
VARIABLE: The
CONDITION: credit the CB account
CAUSE_1: The interest payment order
AND: and
EFFECT_1: shall debit the CLM account holder’s MCA
EFFECT_2: credit the CB account
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Contingency Settlement shall only deliver the information to CLM once CS is closed
### Assistant:
KEYWORD: only
VARIABLE: Contingency Settlement
CONDITION: deliver the information to CLM
CAUSE_1: CS is closed
EFFECT_1: deliver the information to CLM
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
CLM balances shall be taken into account for the calculation mentioned before
### Assistant:
VARIABLE: CLM balances
CONDITION: shall be taken into account
KEYWORD: for
EFFECT_1: CLM balances shall be taken into account
CAUSE_1: the calculation mentioned before
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
CLM shall send the files after the settlement of Standing Facilities
### Assistant:
KEYWORD: after
VARIABLE: CLM
CONDITION: shall send the files
EFFECT_1: CLM shall send the files
CAUSE_1: the settlement of Standing Facilities
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
In case of completeness, CBS shall relay the general ledger files to CBs
### Assistant:
KEYWORD: In case of
VARIABLE: CBS
CONDITION: shall relay the general ledger files to CBs
CAUSE_1: completeness
EFFECT_1: CBS shall relay the general ledger files to CBs
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
With regards to penalties, a feedback from CBs is required before creating the payment orders
### Assistant:
VARIABLE: penalties
CONDITION: creating
KEYWORD: With regards to
CAUSE_1: penalties
EFFECT_1: a feedback from CBs is required
CAUSE_2: creating the payment orders
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Having validated the structure and contents of the document, the below stakeholders are signing-off on their specific business requirements
### Assistant:
VARIABLE: the below stakeholders
AND: and
CONDITION: are signing-off on their specific business requirements
CAUSE_1: Having validated the structure
CAUSE_2: contents of the document
EFFECT_1: the below stakeholders are signing-off on their specific business requirements
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
BoZ has deployed various policies to ensure that financial services are able to make in-roads in remote and rural areas
### Assistant:
VARIABLE: financial services
CONDITION: are able to make in-roads in remote
CAUSE_1: BoZ has deployed various policies
AND: and
KEYWORD: to ensure that
EFFECT_1: financial services are able to make in-roads in remote
EFFECT_2: rural areas
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
A number of initiatives have since been put into motion in order to meet this objective
### Assistant:
VARIABLE: this objective
CONDITION: meet
KEYWORD: in order to
CAUSE_1: A number of initiatives have since been put into motion
EFFECT_1: meet this objective
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
This last mile is where available resources for development are scarcest; where market failures are most pronounced; and where benefits from national growth tend to leave people excluded
### Assistant:
VARIABLE: benefits from national growth
CONDITION: tend to leave people excluded
CAUSE_1: This last mile
KEYWORD: where
NOT_RELEVANT: is
EFFECT_1: available resources for development are scarcest
EFFECT_2: market failures are most pronounced
AND: and
EFFECT_3: benefits from national growth tend to leave people excluded
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
In order to discharge these functions, the Bank needs a robust Data Reporting and Analytics platform (hereinafter “the Platform”) that will support its monitoring and policy making activities
### Assistant:
KEYWORD: that
VARIABLE: the Bank
CONDITION: will support its monitoring
AND: and
CAUSE_1: the Bank needs a robust Data Reporting
CAUSE_2: will support its monitoring
EFFECT_1: discharge these functions
NOT_RELEVANT: (hereinafter “the Platform”)
CAUSE_3: policy making activities
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
BoZ collects a significant magnitude of data from financial service providers to carry out its regulatory obligations and policy making function
### Assistant:
VARIABLE: policy making function
CONDITION: to carry out
AND: and
CAUSE_1: BoZ collects a significant magnitude of data from financial service providers
EFFECT_1: to carry out its regulatory obligations
EFFECT_2: policy making function
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
BoZ seeks to implementing a secure, flexible, scalable solution that automates the data collection, analysis and reporting process
### Assistant:
VARIABLE: reporting process
CONDITION: automates
KEYWORD: that
AND: and
CAUSE_1: BoZ seeks to implementing a secure
CAUSE_2: flexible
CAUSE_3: scalable solution
EFFECT_1: automates the data collection
EFFECT_2: analysis
EFFECT_3: reporting process
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Some of the benefits of the ERP include reduction in complexity of the Bank's Information Architecture through the establishment of a single platform for the Bank's business systems
### Assistant:
KEYWORD: through
VARIABLE: complexity of the Bank's Information Architecture
CONDITION: reduction in
CAUSE_1: the establishment of a single platform for the Bank's business systems
EFFECT_1: reduction in complexity of the Bank's Information Architecture
NOT_RELEVANT: Some of the benefits of the ERP include
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
In the ideal scenario the data warehouse could utilize Online Analytical Processing (OLAP) methodologies for analytical analysis and allowing users to construct queries ad-hoc, or “on-the-fly”
### Assistant:
KEYWORD: for
VARIABLE: users
CONDITION: “on-the-fly”
CAUSE_1: the ideal scenario
AND: and
OR: or
CAUSE_2: the data warehouse could utilize Online Analytical Processing (OLAP) methodologies
EFFECT_1: analytical analysis
EFFECT_2: allowing users to construct queries ad-hoc
EFFECT_3: “on-the-fly”
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Furthermore, the platform should allow it incorporate more features as business needs and technology evolves
### Assistant:
KEYWORD: as
VARIABLE: the platform
CONDITION: should allow it incorporate more features
AND: and
CAUSE_1: business needs
CAUSE_2: technology evolves
NOT_RELEVANT: Furthermore
EFFECT_1: the platform should allow it incorporate more features
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The data will need to reside in the Bank of Zambia in-house server as per Bank of Zambia policies and requirements
### Assistant:
VARIABLE: The data
AND: and
CAUSE_1: Bank of Zambia policies
CAUSE_2: requirements
KEYWORD: as per
CONDITION: will need to reside in the Bank of Zambia in-house server
EFFECT_1: The data will need to reside in the Bank of Zambia in-house server
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
BoZ seeks to enhance the efficiency of the data collection and analytics to ensure quality and timeliness of data
### Assistant:
VARIABLE: timeliness of data
CONDITION: to ensure
AND: and
CAUSE_1: BoZ seeks to enhance the efficiency of the data collection
CAUSE_2: analytics
EFFECT_1: to ensure quality
EFFECT_2: timeliness of data
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
To accommodate the different user groups, the platform will enforce user hierarchies and access controls to prevent unauthorised access to sensitive data and features
### Assistant:
VARIABLE: the different user groups
CONDITION: To accommodate
AND: and
EFFECT_1: To accommodate the different user groups
CAUSE_1: the platform will enforce user hierarchies
CAUSE_2: access controls
EFFECT_2: to prevent unauthorised access to sensitive data
EFFECT_3: features
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
BoZ collects a large amount of data for statistical, prudential and monitoring purposes
### Assistant:
VARIABLE: purposes
CONDITION: monitoring
KEYWORD: for
AND: and
EFFECT_1: BoZ collects a large amount of data
CAUSE_1: statistical
CAUSE_2: prudential
CAUSE_3: monitoring purposes
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
To ensure a harmonized model for input data as well as rules for analysis of collected data, the platform should operate as per the criteria and conditions defined by BoZ in the form of Business Rules
### Assistant:
VARIABLE: the platform
AND: and
CONDITION: conditions defined by BoZ in the form of Business Rules
EFFECT_1: To ensure a harmonized model for input data
EFFECT_2: rules for analysis of collected data
CAUSE_1: the platform should operate as per the criteria
CAUSE_2: conditions defined by BoZ in the form of Business Rules
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
As reporting requirements can evolve with new regulatory priorities, the platform should provide the capability to define and modify business rules through the administrator login
### Assistant:
KEYWORD: As
VARIABLE: the platform
CONDITION: modify business rules through the administrator login
CAUSE_1: reporting requirements can evolve with new regulatory priorities
AND: and
EFFECT_1: the platform should provide the capability to define
EFFECT_2: modify business rules through the administrator login
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The platform will provide additional ad-hoc data access functionality to users so they may run custom queries, according to their access privileges
### Assistant:
VARIABLE: they
CONDITION: according to their access privileges
KEYWORD: so
CAUSE_1: The platform will provide additional ad-hoc data access functionality to users
EFFECT_1: they may run custom queries
CAUSE_2: according to their access privileges
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The design should have a high level of usability with a common “look and feel” achieved through consistent Graphical User Interfaces (GUI) for all internal and external users
### Assistant:
VARIABLE: external users
CONDITION: consistent
KEYWORD: achieved through
CAUSE_1: consistent Graphical User Interfaces (GUI) for all internal
EFFECT_1: The design should have a high level of usability with a common “look and feel”
AND: and
CAUSE_2: external users
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
To ensure integrity of the platform, all its components should have all required security certifications and conform to all industry security standards
### Assistant:
VARIABLE: all its components
CONDITION: conform to all industry security standards
AND: and
EFFECT_1: To ensure integrity of the platform
CAUSE_1: all its components should have all required security certifications
CAUSE_2: conform to all industry security standards
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The platform must be easy to use in order to minimize a chance of human error leading to a malfunctioning of the system
### Assistant:
VARIABLE: a chance of human error leading to a malfunctioning of the system
CONDITION: to minimize
KEYWORD: in order
CAUSE_1: The platform must be easy to use
EFFECT_1: to minimize a chance of human error leading to a malfunctioning of the system
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
At the minimum, the platform has to be available to users with official working hours in Zambia
### Assistant:
VARIABLE: the minimum
CONDITION: has to be available to users with official working hours in Zambia
KEYWORD: At
CAUSE_1: the minimum
EFFECT_1: the platform has to be available to users with official working hours in Zambia
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The platform should provide high reliability and ensure flawless switching of all functionalities to the Disaster Recovery (DR) site to avoid catastrophic loss of critical information
### Assistant:
VARIABLE: catastrophic loss of critical information
CONDITION: to avoid
CAUSE_1: The platform should provide high reliability
AND: and
CAUSE_2: ensure flawless switching of all functionalities to the Disaster Recovery (DR) site
EFFECT_1: to avoid catastrophic loss of critical information
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The platform should be designed with high redundancy level to ensure no or little impact by failure of one or more components
### Assistant:
VARIABLE: more components
CONDITION: to ensure
NEGATION: no
OR: or
CAUSE_1: The platform should be designed with high redundancy level
EFFECT_1: to ensure no
EFFECT_2: little impact by failure of one
EFFECT_3: more components
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
These user requirements were established according to requests formulated by Group ODE taking into account the wishes of our customer, Egbert Teeselink, on behalf of Oc´e
### Assistant:
VARIABLE: the wishes of our customer, Egbert Teeselink
CONDITION: on behalf of Oc´e
EFFECT_1: These user requirements were established according to requests formulated by Group ODE
CAUSE_1: taking into account the wishes of our customer, Egbert Teeselink, on behalf of Oc´e
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Any changes to these requirements require the consent of both parties
### Assistant:
VARIABLE: the consent of both parties
KEYWORD: Any
CONDITION: require
CAUSE_1: changes to these requirements
EFFECT_1: require the consent of both parties
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The ODE Group will provide Oc´e with an editor where the user can model a so called datapath
### Assistant:
VARIABLE: the user
CONDITION: can model a so called datapath
KEYWORD: where
CAUSE_1: The ODE Group will provide Oc´e with an editor
EFFECT_1: the user can model a so called datapath
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The ODE will provide a visual environment in which the user can create and change these models and export them for analysis
### Assistant:
VARIABLE: the user
CONDITION: export them for analysis
KEYWORD: in which
AND: and
CAUSE_1: The ODE will provide a visual environment
EFFECT_1: the user can create
EFFECT_2: change these models
EFFECT_3: export them for analysis
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Because it is irrelevant of which actions the analysis and verification tools consist, it is depicted as an abstract “Analysis” object
### Assistant:
KEYWORD: Because
VARIABLE: it
CONDITION: is depicted as an abstract “Analysis” object
AND: and
CAUSE_1: it is irrelevant of which actions the analysis
CAUSE_2: verification tools consist
EFFECT_1: it is depicted as an abstract “Analysis” object
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The entire structure of the model has yet to be defined and is prone to change after this project has been finished
### Assistant:
KEYWORD: after
VARIABLE: The entire structure of the model
CONDITION: is prone to change
CAUSE_1: this project has been finished
AND: and
EFFECT_1: The entire structure of the model has yet to be defined
EFFECT_2: is prone to change
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
However, some details about the structure of various nodes and edges are known and unlikely to change during the course of this project
### Assistant:
KEYWORD: during
VARIABLE: edges
CAUSE_1: the course of this project
NOT_RELEVANT: However,
AND: and
CONDITION: unlikely to change
EFFECT_1: some details about the structure of various nodes
EFFECT_2: edges are known
EFFECT_3: unlikely to change
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Because the export format and the input language are prone to modifications, it is important that future developers can easily modify the code to comply with new standards
### Assistant:
KEYWORD: it is important that
VARIABLE: future developers
AND: and
CONDITION: can easily modify the code to comply with new standards
CAUSE_1: the export format
CAUSE_2: the input language are prone to modifications
EFFECT_1: future developers can easily modify the code to comply with new standards
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
However, it may check for basic validity of models created with the ODE and provide constraints within its GUI to prevent some basic syntactical errors
### Assistant:
NOT_RELEVANT: However
VARIABLE: some basic syntactical errors
CONDITION: provide constraints within its GUI
AND: and
NEGATION: to prevent
CAUSE_1: it may check for basic validity of models created with the ODE
CAUSE_2: provide constraints within its GUI
EFFECT_1: to prevent some basic syntactical errors
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
These users are also very important since the export format and the product’s required functionality are prone to change
### Assistant:
VARIABLE: the product’s required functionality
CONDITION: are prone to change
KEYWORD: since
AND: and
EFFECT_1: These users are also very important
CAUSE_1: the export format
CAUSE_2: the product’s required functionality are prone to change
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
In order for us to be able to provide a good product, we expect the customer to comply to several requirements
### Assistant:
KEYWORD: In order for
VARIABLE: we
CONDITION: expect the customer to comply to several requirements
EFFECT_1: us to be able to provide a good product
CAUSE_1: we expect the customer to comply to several requirements
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
It is possible to select any application node or platform node by means of a method call
### Assistant:
KEYWORD: by
VARIABLE: It
CAUSE_1: means of a method call
CONDITION: platform node
OR: or
EFFECT_1: It is possible to select any application node
EFFECT_2: platform node
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
It is possible to highlight any application node or platform node by means of a method call
### Assistant:
KEYWORD: by
CAUSE_1: means of a method call
VARIABLE: It
CONDITION: platform node
OR: or
EFFECT_1: It is possible to highlight any application node
EFFECT_2: platform node
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
Other programs can send messages to the product for selecting and highlighting edges and nodes
### Assistant:
VARIABLE: nodes
CONDITION: highlighting
KEYWORD: for
AND: and
CAUSE_1: Other programs can send messages to the product
EFFECT_1: selecting
EFFECT_2: highlighting edges
EFFECT_3: nodes
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
The editor will assign an algorithm to a link if there is exactly one algorithm available for it
### Assistant:
KEYWORD: if
VARIABLE: there
CONDITION: is exactly one algorithm available for it
EFFECT_1: The editor will assign an algorithm to a link
CAUSE_1: there is exactly one algorithm available for it
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
There is a unit test for every method or class that is not directly responding to a mouse-click or keystroke
### Assistant:
KEYWORD: that
VARIABLE: There
CONDITION: keystroke
OR: or
EFFECT_1: There is a unit test for every method
EFFECT_2: class
CAUSE_1: is not directly responding to a mouse-click
CAUSE_2: keystroke
NEGATION: is not
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
The ODE does not measurably suffer from memory leaks
### Assistant:
VARIABLE: memory leaks
NEGATION: does not
CONDITION: measurably suffer
KEYWORD: from
EFFECT_1: The ODE does not measurably suffer
CAUSE_1: memory leaks
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
The requirements in the OAD (RD2) will flow down to requirements for the observatory subsystems
### Assistant:
VARIABLE: the observatory subsystems
CONDITION: will flow down to requirements
KEYWORD: for
CAUSE_1: will flow down to requirements
EFFECT_1: the observatory subsystems
NOT_RELEVANT: (RD2)
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
This numbering scheme allows for unambiguous reference to requirements
### Assistant:
VARIABLE: unambiguous reference to requirements
CONDITION: allows for
CAUSE_1: This numbering scheme
EFFECT_1: allows for unambiguous reference to requirements
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Early operations is defined as the period that starts when the first nontechnical facilities are accepted and continues until the observatory has reached steady-state operations
### Assistant:
VARIABLE: the observatory
CONDITION: is defined as the period
AND: and
NEGATION: until
KEYWORD: continues
EFFECT_1: Early operations is defined as the period
CAUSE_1: the first nontechnical facilities are accepted
CAUSE_2: the observatory has reached steady-state operations
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
Early science operations are expected to begin during the first 12 months after the M1 has been fully populated and phased for the first time
### Assistant:
VARIABLE: the M1
CONDITION: phased for the first time
KEYWORD: after
AND: and
EFFECT_1: Early science operations are expected to begin
CAUSE_1: the first 12 months
CAUSE_3: phased for the first time
CAUSE_2: the M1 has been fully populated
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Steady-state (science) operations is defined as the period that starts 36 (TBC) months after the M1 has been fully populated and phased for the first time
### Assistant:
VARIABLE: the period
KEYWORD: that
CONDITION: months after the M1 has been fully populated
AND: and
EFFECT_1: Steady-state (science) operations
CAUSE_1: months after the M1 has been fully populated
CAUSE_2: phased for the first time
NOT_RELEVANT: (TBC)
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Nighttime is defined as the time between the end of evening nautical twilight and the beginning of morning nautical twilight
### Assistant:
VARIABLE: the time
KEYWORD: is defined as
CONDITION: between the end of evening nautical twilight and the beginning of morning nautical twilight.
EFFECT_1: Nighttime
CAUSE_1: the time between the end of evening nautical twilight and the beginning of morning nautical twilight
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
At these points, the center of the sun is 12 degrees below the horizon
### Assistant:
VARIABLE: these points
CONDITION: is 12 degrees below the horizon
CAUSE_1: these points
EFFECT_1: the center of the sun is 12 degrees below the horizon
KEYWORD: At
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
For Mauna Kea, this time interval corresponds to roughly 10 hours per 24-hour period in the mean
### Assistant:
VARIABLE: this time interval
KEYWORD: For
CONDITION: corresponds to roughly 10 hours per 24-hour period in the mean
CAUSE_1: Mauna Kea
EFFECT_1: this time interval corresponds to roughly 10 hours per 24-hour period in the mean
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Conceptually, a high-impact paper contains transformational and/or unique scientific results
### Assistant:
VARIABLE: unique scientific results
CONDITION: contains
NOT_RELEVANT: Conceptually
AND: and
OR: or
EFFECT_1: a high-impact paper
CAUSE_1: contains transformational
CAUSE_2: unique scientific results
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
High-impact papers often result from exploiting unique observatory capabilities
### Assistant:
VARIABLE: unique observatory capabilities
KEYWORD: often result from
CONDITION: High-impact
CAUSE_1: exploiting unique observatory capabilities
EFFECT_1: High-impact papers
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Conversely, it is well recognized that observatories that do not add new capabilities over time quickly become scientifically irrelevant, often limiting the overall scientific return on the original capital investment
### Assistant:
VARIABLE: the overall scientific return on the original capital investment
KEYWORD: that
CONDITION: often limiting
CAUSE_1: do not add new capabilities over time
EFFECT_1: quickly become scientifically irrelevant
EFFECT_2: often limiting the overall scientific return on the original capital investment
NOT_RELEVANT: Conversely, it is well recognized that
NEGATION: do not
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
To maintain its lead in as many areas as possible within available funding, the TMT Observatory will execute a vigorous development program during the operations phase
### Assistant:
VARIABLE: the operations phase
CONDITION: will execute a vigorous development program
EFFECT_1: To maintain its lead in as many areas as possible within available funding
CAUSE_1: the TMT Observatory will execute a vigorous development program
KEYWORD: during
CAUSE_2: the operations phase
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
As the number of science integration hours decreases, so does the potential for transformational observations
### Assistant:
KEYWORD: so
VARIABLE: the potential for transformational observations
CONDITION: does
CAUSE_1: the number of science integration hours decreases
EFFECT_1: does the potential for transformational observations
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Discussion: Calibrations which are specific to an observing program and taken at night will be accounted in PI/partners time
### Assistant:
NOT_RELEVANT: Discussion:
VARIABLE: Calibrations
KEYWORD: which
CONDITION: will be accounted in PI/partners time
AND: and
CAUSE_1: are specific to an observing program
CAUSE_2: taken at night
EFFECT_1: will be accounted in PI/partners time
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The TMT Observatory at the early light stage shall provide as a minimum two observing modes; PI Directed (Classical) Observing Mode and Pre-Planned Queue Service Observing
### Assistant:
VARIABLE: Pre-Planned Queue Service Observing
CONDITION: shall provide as a minimum two observing modes
CAUSE_1: The TMT Observatory at the early light stage
EFFECT_1: shall provide as a minimum two observing modes
EFFECT_2: PI Directed (Classical) Observing Mode
AND: and
EFFECT_3: Pre-Planned Queue Service Observing
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The TMT Observatory shall provide a service queue mode that includes synoptic and cadence observations
### Assistant:
VARIABLE: cadence observations
CONDITION: includes
CAUSE_1: The TMT Observatory shall provide a service queue mode
KEYWORD: that
AND: and
EFFECT_1: includes synoptic
EFFECT_2: cadence observations
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The TMT Observatory shall enable real-time selection and execution of observations with flexibility in response to changing conditions and ongoing observations
### Assistant:
VARIABLE: observations
CONDITION: ongoing
AND: and
KEYWORD: in response to
CAUSE_1: changing conditions
CAUSE_2: ongoing observations
EFFECT_1: The TMT Observatory shall enable real-time selection
EFFECT_2: execution of observations with flexibility
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The TMT Observatory OB shall contain all the user-specific information necessary for executing and scheduling an observation
### Assistant:
VARIABLE: an observation
CONDITION: scheduling
KEYWORD: for
AND: and
CAUSE_1: The TMT Observatory OB shall contain all the user-specific information necessary
EFFECT_1: executing
EFFECT_2: scheduling an observation
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The TMT observatory shall provide an observing process that enables the observer to check their OBs and observing scripts off-line before observing
### Assistant:
VARIABLE: observing
CONDITION: observing scripts off-line
KEYWORD: before
AND: and
CAUSE_1: The TMT observatory shall provide an observing process
EFFECT_1: enables the observer to check their OBs
EFFECT_2: observing scripts off-line
CAUSE_2: observing
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Discussion: Backup observing programs will be prepared for all TMT observing service queue programs in the event that weather conditions or technical failures prevent the execution of the primary science program
### Assistant:
NOT_RELEVANT: Discussion:
VARIABLE: all TMT observing service queue programs
CONDITION: the execution of the primary science program
KEYWORD: for
OR: or
NEGATION: prevent
CAUSE_2: weather conditions
CAUSE_3: technical failures prevent the execution of the primary science program
EFFECT_1: Backup observing programs will be prepared
CAUSE_1: all TMT observing service queue programs
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The TMT Observatory in steady-state operations shall have no more than 3% unscheduled technical downtime between the end of evening nautical twilight and the start of morning nautical twilight during hours scheduled for science operations
### Assistant:
VARIABLE: operations
CONDITION: more than 3% unscheduled technical downtime between the end of evening nautical twilight and the start of morning nautical twilight
KEYWORD: during
CAUSE_1: steady-state operations
EFFECT_1: shall have no more than 3% unscheduled technical downtime between the end of evening nautical twilight and the start of morning nautical twilight
CAUSE_2: hours scheduled for science operations
NEGATION: shall have no
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Discussion: Scheduled technical time will be publicly announced and taken into account when the observatory science operations schedule is constructed
### Assistant:
CONDITION: is constructed
NOT_RELEVANT: Discussion:
VARIABLE: the observatory science operations schedule
AND: and
KEYWORD: when
EFFECT_1: Scheduled technical time will be publicly announced
EFFECT_2: taken into account
CAUSE_1: the observatory science operations schedule is constructed
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Discussion: All instruments declared to be operational are expected to have unscheduled technical downtimes commensurate with the above system level requirement
### Assistant:
NOT_RELEVANT: Discussion:
VARIABLE: All instruments
CONDITION: are expected to have unscheduled technical downtimes commensurate with the above system level requirement
CAUSE_1: All instruments declared to be operational
EFFECT_1: are expected to have unscheduled technical downtimes commensurate with the above system level requirement
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The TMT Observatory shall monitor operational metrics to identify potential improvements and monitor observing efficiency and other telescope performance metrics
### Assistant:
VARIABLE: other telescope performance metrics
CONDITION: monitor
AND: and
CAUSE_1: The TMT Observatory shall monitor operational metrics
EFFECT_1: to identify potential improvements
EFFECT_2: monitor observing efficiency
EFFECT_3: other telescope performance metrics
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Discussion: Baseline image quality performance will be established during TMT integration and verification
### Assistant:
NOT_RELEVANT: Discussion:
VARIABLE: verification
CONDITION: will be established
KEYWORD: during
AND: and
EFFECT_1: Baseline image quality performance will be established
CAUSE_1: TMT integration
CAUSE_2: verification
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Access to that area will require user authentication
### Assistant:
VARIABLE: user authentication
CONDITION: will require
CAUSE_1: Access to that area
EFFECT_1: will require user authentication
### END
|