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### Instruction:
Extract the cause and effect entities from the following sentence:
For the LI: A single spectral channel is defined, thus each spatial sample has an associated radiance sample
### Assistant:
VARIABLE: each spatial sample
NOT_RELEVANT: For the LI:
CONDITION: has an associated radiance sample
KEYWORD: thus
CAUSE_1: A single spectral channel is defined
EFFECT_1: each spatial sample has an associated radiance sample
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
In the case of the IRS the PSF of the spatial sample will vary with the spectral sample or spectral channel, although the nominal location will be the same across the spectral variable range
### Assistant:
KEYWORD: In the case of
VARIABLE: the nominal location
CONDITION: will be the same across the spectral variable range
OR: or
AND: although
CAUSE_1: the IRS
EFFECT_1: the PSF of the spatial sample will vary with the spectral sample
EFFECT_2: spectral channel
EFFECT_3: the nominal location will be the same across the spectral variable range
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
The spectral sample, identified by the index k, has an associated spectral variable, ξ k, where this location corresponds to the position of its spectral response function centroid
### Assistant:
VARIABLE: this location
CONDITION: corresponds to the position of its spectral response function centroid
KEYWORD: where
CAUSE_1: The spectral sample, identified by the index k, has an associated spectral variable, ξ k,
EFFECT_1: this location corresponds to the position of its spectral response function centroid
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
As the IRS will perform a number of spectral soundings simultaneously there will be a scale change between the spectral spacing of the spectral samples for each spatial sample
### Assistant:
VARIABLE: there
CONDITION: will be a scale change between the spectral spacing of the spectral samples for each spatial sample
KEYWORD: As
CAUSE_1: the IRS will perform a number of spectral soundings simultaneously
EFFECT_1: there will be a scale change between the spectral spacing of the spectral samples for each spatial sample
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
The specified spectral width, Δλ0 will be different from the actual spectral width, Δλs, of a spectral channel, where the actual spectral width has traditionally been defined as the Full Width Half Maximum value of the spectral response function, ignoring local oscillations in the passband
### Assistant:
VARIABLE: the actual spectral width
CONDITION: ignoring local oscillations in the passband
KEYWORD: where
EFFECT_1: The specified spectral width, Δλ0 will be different from the actual spectral width, Δλs, of a spectral channel
CAUSE_1: the actual spectral width has traditionally been defined as the Full Width Half Maximum value of the spectral response function
CAUSE_2: ignoring local oscillations in the passband
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
The specified central wavelength, λ 0, will be different from the actual central wavelength, λ S, of a spectral channel, where the actual central wavelength has traditionally been defined as the centroid of the spectral response function
### Assistant:
VARIABLE: the actual central wavelength
CONDITION: has traditionally been defined as the centroid of the spectral response function
KEYWORD: where
EFFECT_1: The specified central wavelength, λ 0, will be different from the actual central wavelength, λ S, of a spectral channel
CAUSE_1: the actual central wavelength has traditionally been defined as the centroid of the spectral response function
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
For the IRS: Spectral resampling is the process by which the spectral samples derived from a sampled interferogram are relocated to pre-determined wavenumber locations, thus forming spectral channels
### Assistant:
NOT_RELEVANT: For the IRS:
VARIABLE: spectral channels
CONDITION: forming
KEYWORD: thus
CAUSE_1: Spectral resampling is the process by which the spectral samples derived from a sampled interferogram are relocated to pre-determined wavenumber locations
EFFECT_1: forming spectral channels
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
In the definition of IRF the instruments are assumed to be linear
### Assistant:
VARIABLE: the definition of IRF
CONDITION: are assumed to be linear
CAUSE_1: the definition of IRF
EFFECT_1: the instruments are assumed to be linear
KEYWORD: In
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
For a given spectral interval, albedo is the ratio between the power per unit surface area emitted by a surface and the power per unit surface area incident on the surface
### Assistant:
KEYWORD: For
VARIABLE: albedo
CONDITION: is the ratio between the power per unit surface area emitted by a surface and the power per unit surface area incident on the surface
CAUSE_1: a given spectral interval
EFFECT_1: albedo is the ratio between the power per unit surface area emitted by a surface and the power per unit surface area incident on the surface
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
When expressed in this form the radiometric noise is given as noise equivalent delta radiance (NEdL)
### Assistant:
KEYWORD: When
CONDITION: is given as noise equivalent delta radiance (NEdL)
VARIABLE: the radiometric noise
CAUSE_1: expressed in this form
EFFECT_1: the radiometric noise is given as noise equivalent delta radiance (NEdL)
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
For the VIS/NIR spectral channels, the radiometric noise can be given in terms of signal to noise ratio (SNR) associated with a signal at which the SNR is computed
### Assistant:
KEYWORD: For
VARIABLE: the radiometric noise
CONDITION: can be given in terms of signal to noise ratio (SNR) associated with a signal at which the SNR is computed
CAUSE_1: the VIS/NIR spectral channels
EFFECT_1: the radiometric noise can be given in terms of signal to noise ratio (SNR) associated with a signal at which the SNR is computed
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
Radiometric noise applies to radiometrically calibrated spectra, meaning that the noise induced by radiometric calibration is included
### Assistant:
VARIABLE: the noise induced by radiometric calibration
CONDITION: is included
CAUSE_1: Radiometric noise applies to radiometrically calibrated spectra
KEYWORD: meaning that
EFFECT_1: the noise induced by radiometric calibration is included
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
For the FCI IR spectral channels the medium term radiometric stability is the radiometric stability between any two images lying in the interval between two calibration cycles
### Assistant:
KEYWORD: For
VARIABLE: the medium term radiometric stability
CONDITION: is the radiometric stability between any two images lying in the interval between two calibration cycles
CAUSE_1: the FCI IR spectral channels
EFFECT_1: the medium term radiometric stability is the radiometric stability between any two images lying in the interval between two calibration cycles
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
A triggered event occurs when the energy registered by a detector element exceeds the LI trigger threshold
### Assistant:
VARIABLE: the energy registered by a detector element
CONDITION: exceeds the LI trigger threshold
KEYWORD: when
EFFECT_1: A triggered event occurs
CAUSE_1: the energy registered by a detector element exceeds the LI trigger threshold
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
The trigger threshold is used, at detector element level to discriminate a lightning optical pulse from the background radiance
### Assistant:
VARIABLE: detector element level
CONDITION: to discriminate
CAUSE_1: The trigger threshold is used
EFFECT_1: to discriminate a lightning optical pulse from the background radiance
KEYWORD: at
CAUSE_2: detector element level
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
The spatial sample, identified by the indices (i,j), is spatially located at (xi,yj), where this location corresponds to the position of its point spread function centroid
### Assistant:
VARIABLE: this location
CONDITION: corresponds to the position of its point spread function centroid
KEYWORD: where
CAUSE_1: The spatial sample, identified by the indices (i,j), is spatially located at (xi,yj)
EFFECT_1: this location corresponds to the position of its point spread function centroid
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
In the definition of PSF the instruments are assumed to be linear
### Assistant:
VARIABLE: the definition of PSF
CONDITION: are assumed to be linear
CAUSE_1: the definition of PSF
EFFECT_1: the instruments are assumed to be linear
KEYWORD: In
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
In order to achieve this transformation the radiance samples associated with the level 1b spatial samples are interpolated from the estimated grid to the reference grid to give radiance samples for each level 1c pixel
### Assistant:
KEYWORD: In order
CONDITION: associated with the level 1b spatial samples
VARIABLE: radiance samples for each level 1c pixel
EFFECT_1: to achieve this transformation
CAUSE_1: the radiance samples associated with the level 1b spatial samples
EFFECT_2: to give radiance samples for each level 1c pixel
CAUSE_2: are interpolated from the estimated grid to the reference grid
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
When used to derive the position of future spatial samples based on past information the estimated grid is generated
### Assistant:
KEYWORD: When
CONDITION: is generated
VARIABLE: the estimated grid
CAUSE_1: used to derive the position of future spatial samples based on past information
EFFECT_1: the estimated grid is generated
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
The pixel is constructed from a number of spatial samples that have been interpolated to the given reference grid location during the image rectification process
### Assistant:
VARIABLE: The pixel
KEYWORD: during
CONDITION: is constructed
NOT_RELEVANT: from
EFFECT_1: The pixel is constructed
CAUSE_1: have been interpolated to the given reference grid location
CAUSE_2: the image rectification process
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
In practice the location of the geographic features will be to sub-SSD accuracy
### Assistant:
CONDITION: will be to sub-SSD accuracy
VARIABLE: the location of the geographic features
CAUSE_1: In practice
EFFECT_1: the location of the geographic features will be to sub-SSD accuracy
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
Coregistration is used to describe the relative position of the spatial samples or pixels between different spectral channels of an instrument or between instruments
### Assistant:
VARIABLE: between instruments
CONDITION: to describe
OR: or
CAUSE_1: Coregistration is used
EFFECT_1: to describe the relative position of the spatial samples
EFFECT_2: pixels between different spectral channels of an instrument
EFFECT_3: between instruments
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
When the RPPKE between spectral channels with differing spatial sampling distance is A group of p2 of the finer resolution spectral channel pixels centred on a coarse resolution pixel is taken as a pixel group
### Assistant:
KEYWORD: When
VARIABLE: pixels centred on a coarse resolution pixel
CONDITION: is taken as a pixel group
CAUSE_1: the RPPKE between spectral channels with differing spatial sampling distance is A group of p2 of the finer resolution spectral channel
EFFECT_1: pixels centred on a coarse resolution pixel is taken as a pixel group
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
Due to the properties of the reference grid this means that the fine resolution pixel group and coarse resolution pixel are located at the same position
### Assistant:
KEYWORD: this means that
VARIABLE: coarse resolution pixel
AND: and
CONDITION: are located at the same position
CAUSE_1: the properties of the reference grid
EFFECT_1: the fine resolution pixel group
EFFECT_2: coarse resolution pixel are located at the same position
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Eclipse is defined as when the solar disk is occulted by the Earth or Moon, as viewed from the MTG satellite
### Assistant:
VARIABLE: the solar disk
KEYWORD: is defined as when
CONDITION: Moon
OR: or
NOT_RELEVANT: as viewed from the MTG satellite
EFFECT_1: Eclipse
CAUSE_1: the solar disk is occulted by the Earth
CAUSE_2: Moon
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The eclipse start is the time at which the sun starts to be occulted by the Earth or moon
### Assistant:
VARIABLE: the sun
NOT_RELEVANT: is the time at which
CONDITION: moon
OR: or
EFFECT_1: The eclipse start
CAUSE_1: the sun starts to be occulted by the Earth
CAUSE_2: moon
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The solar restricted zones are applicable for the uneclipsed and partially eclipsed sun
### Assistant:
VARIABLE: the
CONDITION: partially eclipsed
KEYWORD: for
AND: and
EFFECT_1: The solar restricted zones are applicable
CAUSE_1: the uneclipsed
CAUSE_2: partially eclipsed sun
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
It is defined to allow graded relaxation of requirements for spatial samples and pixels located close to the sun
### Assistant:
VARIABLE: pixels located close to the sun
CONDITION: to allow
AND: and
CAUSE_1: It is defined
EFFECT_1: to allow graded relaxation of requirements for spatial samples
EFFECT_2: pixels located close to the sun
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
All trigonometric functions assume angles in degree
### Assistant:
VARIABLE: angles
CONDITION: in degree
CAUSE_1: All trigonometric functions
EFFECT_1: assume angles in degree
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
The potential use during routine operations depends on experience in orbit and recommendation of the OPS-WG
### Assistant:
KEYWORD: during
VARIABLE: routine operations
CONDITION: recommendation of the OPS-WG
AND: and
EFFECT_1: depends on experience in orbit
CAUSE_1: routine operations
EFFECT_2: recommendation of the OPS-WG
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
The LI shall provide a spatial sampling distance less than or equal to 10km at 45oN for the sub-satellite longitude
### Assistant:
VARIABLE: 45oN
CONDITION: equal to 10km
OR: or
KEYWORD: at
EFFECT_1: The LI shall provide a spatial sampling distance less than
EFFECT_2: equal to 10km
CAUSE_1: 45oN
CAUSE_2: the sub-satellite longitude
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
Modification affecting only s are not tracked in the statistics but still listed above for completeness
### Assistant:
VARIABLE: Modification
CONDITION: still listed above for completeness
NEGATION: are not
AND: but
CAUSE_1: Modification affecting only s
EFFECT_1: are not tracked in the statistics
EFFECT_2: still listed above for completeness
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
Numbering is not necessarily sequential; gaps in the sequence leave room to add additional related requirements when they are discovered
### Assistant:
VARIABLE: they
CONDITION: are discovered
KEYWORD: when
NOT_RELEVANT: Numbering is not necessarily sequential;
CAUSE_1: they are discovered
EFFECT_1: gaps in the sequence leave room to add additional related requirements
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
These quality requirements pertain directly to maintaining a high level of service quality
### Assistant:
VARIABLE: a high level of service quality
CONDITION: to maintaining
CAUSE_1: These quality requirements pertain directly
EFFECT_1: to maintaining a high level of service quality
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
In order to enable reusability and commonality across the EAN.UCC Business Message Standards, a common library has been developed to support the technical implementation of the EAN.UCC standards into XML schemas resulting in standardized data types and field sizes
### Assistant:
KEYWORD: In order
CONDITION: to support
VARIABLE: the technical implementation of the EAN.UCC standards into XML schemas resulting in standardized data types and field sizes
AND: and
EFFECT_1: to enable reusability
EFFECT_2: commonality across the EAN.UCC Business Message Standards
CAUSE_1: a common library has been developed
EFFECT_3: to support the technical implementation of the EAN.UCC standards into XML schemas resulting in standardized data types and field sizes
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
These attributes in combination ensure the uniqueness of the data set associated with a GTIN
### Assistant:
VARIABLE: the uniqueness of the data set associated with a GTIN
CONDITION: ensure
CAUSE_1: These attributes in combination
EFFECT_1: ensure the uniqueness of the data set associated with a GTIN
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Additionally, the existing Electronic Data Interchange messages in widespread use were mined for their business content
### Assistant:
NOT_RELEVANT: Additionally,
VARIABLE: their business content
CONDITION: were mined
KEYWORD: for
EFFECT_1: the existing Electronic Data Interchange messages in widespread use were mined
CAUSE_1: their business content
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Following the establishment of the trading agreement the parties must exchange their basic business data such as trading partner names, addresses, locations, item attributes, price lists, contracts and trading partner agreements
### Assistant:
VARIABLE: the parties
CONDITION: trading partner agreements
CAUSE_1: Following the establishment of the trading agreement
AND: and
EFFECT_1: the parties must exchange their basic business data such as trading partner names, addresses, locations, item attributes, price lists, contracts
EFFECT_2: trading partner agreements
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
This process creates a common understanding between the trading parties which can be used as a resource throughout the trading process
### Assistant:
VARIABLE: This process
CONDITION: can be used as a resource throughout the trading process
KEYWORD: which
CAUSE_1: This process creates a common understanding between the trading parties
EFFECT_1: can be used as a resource throughout the trading process
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
The objective of this document is to elaborate the Data Synchronization Data Model for Trade Item (hereafter referred to as ‘Data Sync Trade Item’) business process in enough detail to support the construction of standards
### Assistant:
VARIABLE: the construction of standards
CONDITION: business process in enough detail
CAUSE_1: business process in enough detail
EFFECT_1: to support the construction of standards
NOT_RELEVANT: (hereafter referred to as ‘Data Sync Trade Item’)
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The Data Sync Trade Item business process begins when the parties decide to do business together
### Assistant:
VARIABLE: the parties
CONDITION: decide to do business together
KEYWORD: when
EFFECT_1: The Data Sync Trade Item business process begins
CAUSE_1: the parties decide to do business together
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The start-state of the Data Sync Trade Item business process begins during the initial discussions between the trading partners and the need to exchange item information
### Assistant:
VARIABLE: the
CONDITION: initial
KEYWORD: during
AND: and
EFFECT_1: The start-state of the Data Sync Trade Item business process begins
CAUSE_1: the initial discussions between the trading partners
CAUSE_2: the need to exchange item information
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Once the Data Sync Trade Item business message has been accepted by both the seller and buyer, then data alignment has been achieved
### Assistant:
KEYWORD: then
VARIABLE: data alignment
CONDITION: buyer
CAUSE_1: the seller
EFFECT_1: data alignment has been achieved
AND: and
CAUSE_2: buyer
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
This process can be an ongoing process as item business information changes or new parties are added
### Assistant:
VARIABLE: new parties
CONDITION: are added
KEYWORD: as
OR: or
EFFECT_1: This process can be an ongoing process
CAUSE_1: item business information changes
CAUSE_2: new parties are added
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The end-state of the Data Sync Trade Item business process occurs when the parties have achieved Party and Item data alignment
### Assistant:
VARIABLE: the parties
CONDITION: Item data alignment
KEYWORD: when
AND: and
EFFECT_1: The end-state of the Data Sync Trade Item business process occurs
CAUSE_1: the parties have achieved Party
CAUSE_2: Item data alignment
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
To help with the implementation of this data model, several key definitions are listed below
### Assistant:
CONDITION: are listed below
VARIABLE: several key definitions
EFFECT_1: To help with the implementation of this data model
CAUSE_1: several key definitions are listed below
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Master data is considered to be that set of data that describes the specifications and structure of each item and party involved in the supply chain processes
### Assistant:
VARIABLE: set of data
CONDITION: party involved in the supply chain processes
AND: and
CAUSE_1: describes the specifications
KEYWORD: that
CAUSE_2: structure of each item
CAUSE_3: party involved in the supply chain processes
EFFECT_1: Master data
NOT_RELEVANT: is considered to be that
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Transactional data is that set of data that can only be determined during a business transaction, i.e., plan, sell, buy, and deliver cycle
### Assistant:
VARIABLE: set of data
EFFECT_1: Transactional data
KEYWORD: that
CONDITION: can only be determined during a business transaction
NOT_RELEVANT: i.e., plan, sell, buy, and deliver cycle
CAUSE_1: can only be determined during a business transaction
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Values for an attribute can vary depending on the relationship with the party receiving the data
### Assistant:
VARIABLE: the relationship with the party receiving the data
CONDITION: can vary
KEYWORD: depending on
EFFECT_1: Values for an attribute can vary
CAUSE_1: the relationship with the party receiving the data
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The condition Trading Partner Neutral is applied to any attribute whose value is independent on a buyer and seller relationship
### Assistant:
VARIABLE: value
CONDITION: is independent on a buyer and seller relationship
EFFECT_1: The condition Trading Partner Neutral is applied to any attribute
KEYWORD: whose
CAUSE_1: value is independent on a buyer and seller relationship
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
An attribute, which has the condition Trading Partner Neutral, can have only one set of values
### Assistant:
VARIABLE: An attribute
KEYWORD: which
CONDITION: can have only one set of values
CAUSE_1: has the condition Trading Partner Neutral
EFFECT_1: can have only one set of values
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The condition Trading Partner Dependent is applied to any attribute whose value is dependent on a buyer and seller relationship
### Assistant:
VARIABLE: value
CONDITION: is dependent on a buyer and seller relationship
KEYWORD: whose
EFFECT_1: The condition Trading Partner Dependent is applied to any attribute
CAUSE_1: value is dependent on a buyer and seller relationship
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
An attribute, which has the condition Trading Partner Dependent, can have only one set of values per GLN of Party Receiving Data
### Assistant:
VARIABLE: An attribute
KEYWORD: which
CONDITION: can have only one set of values per GLN of Party Receiving Data
CAUSE_1: has the condition Trading Partner Dependent
EFFECT_1: can have only one set of values per GLN of Party Receiving Data
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
This condition is used to indicate that multiple values can be submitted for a trade item
### Assistant:
VARIABLE: that multiple values can be submitted for a trade item
CONDITION: to indicate
CAUSE_1: This condition is used
EFFECT_1: to indicate that multiple values can be submitted for a trade item
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Where there is a need to express multiple measures, these are handled through indicating the attribute type as ‘Measurement’
### Assistant:
KEYWORD: Where
VARIABLE: these
CONDITION: are handled through indicating the attribute type as ‘Measurement’
CAUSE_1: there is a need to express multiple measures
EFFECT_1: these are handled through indicating the attribute type as ‘Measurement’
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Where there is a need to express multiple language versions, these are handled through indicating the attribute type as ‘Text Description’
### Assistant:
KEYWORD: Where
VARIABLE: these
CONDITION: are handled through indicating the attribute type as ‘Text Description’
CAUSE_1: there is a need to express multiple language versions
EFFECT_1: these are handled through indicating the attribute type as ‘Text Description’
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
A Market Group is used to identify a proprietary group of data recipients
### Assistant:
VARIABLE: a proprietary group of data recipients
CONDITION: to identify
CAUSE_1: A Market Group is used
EFFECT_1: to identify a proprietary group of data recipients
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Target Market Country Code in relationship to Master Data, can be Trading Partner Neutral only
### Assistant:
VARIABLE: Target Market Country Code
CONDITION: can be Trading Partner Neutral only
CAUSE_1: Target Market Country Code in relationship to Master Data
EFFECT_1: can be Trading Partner Neutral only
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
This condition can be used by information providers as they build data models
### Assistant:
VARIABLE: they
CONDITION: build data models
KEYWORD: as
EFFECT_1: This condition can be used by information providers
CAUSE_1: they build data models
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
With this information, they can limit the attributes communicated to those relevant for that geography
### Assistant:
KEYWORD: With
VARIABLE: they
CONDITION: can limit the attributes communicated to those relevant for that geography
CAUSE_1: this information
EFFECT_1: they can limit the attributes communicated to those relevant for that geography
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
A global/local attribute indicates that the field is relevant for business cases around the world, that its definition is the same around the world, but may have a different value depending on the geography
### Assistant:
VARIABLE: its definition
CONDITION: may have a different value depending on the geography
OR: /
CAUSE_1: A global
CAUSE_2: local attribute
NOT_RELEVANT: that
AND: but
EFFECT_1: the field is relevant for business cases around the world
EFFECT_2: its definition is the same around the world
EFFECT_3: may have a different value depending on the geography
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
An attribute in this model is mandatory when its value is required
### Assistant:
VARIABLE: its value
CONDITION: is required
KEYWORD: when
EFFECT_1: An attribute in this model is mandatory
CAUSE_1: its value is required
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
An attribute in this model is Optional when its value is not required
### Assistant:
VARIABLE: its value
CONDITION: required
KEYWORD: when
NEGATION: is not
EFFECT_1: An attribute in this model is Optional
CAUSE_1: its value is not required
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
This condition is used to indicate at what level of the product hierarchy each attribute is relevant
### Assistant:
VARIABLE: at what level of the product hierarchy each attribute is relevant
CONDITION: to indicate
CAUSE_1: This condition is used
EFFECT_1: to indicate at what level of the product hierarchy each attribute is relevant
### END
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### Instruction:
Extract the cause and effect entities from the following sentence:
For some attributes, business requirements are such that the attribute only needs to be provided at a specific level
### Assistant:
KEYWORD: For
CONDITION: are such that the attribute only needs to be provided at a specific level
VARIABLE: business requirements
CAUSE_1: some attributes
EFFECT_1: business requirements are such that the attribute only needs to be provided at a specific level
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
For most attributes, a value must be entered for all attribute levels
### Assistant:
KEYWORD: For
VARIABLE: a value
CONDITION: must be entered for all attribute levels
CAUSE_1: most attributes
EFFECT_1: a value must be entered for all attribute levels
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Common value condition indicates when the value for the attribute is equal for all levels of a hierarchy
### Assistant:
VARIABLE: the value for the attribute
CONDITION: is equal for all levels of a hierarchy
KEYWORD: when
EFFECT_1: Common value condition indicates
CAUSE_1: the value for the attribute is equal for all levels of a hierarchy
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If the item is not mixed, then this number will equal the value submitted for the “QuantityofNextLowerLevelTradeItem” field
### Assistant:
KEYWORD: then
VARIABLE: this number
NEGATION: is not
CONDITION: will equal the value submitted for the “QuantityofNextLowerLevelTradeItem” field
CAUSE_1: the item is not mixed
EFFECT_1: this number will equal the value submitted for the “QuantityofNextLowerLevelTradeItem” field
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The sequence of entering GTINs is important when a next lower level GTIN is to be linked
### Assistant:
VARIABLE: a next lower level GTIN
CONDITION: is to be linked
KEYWORD: when
EFFECT_1: The sequence of entering GTINs is important
CAUSE_1: a next lower level GTIN is to be linked
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The next lower level GTIN must already exist, therefore the order of trade items to be entered should be from lowest to highest e.g. consumer, then intermediate, then traded, etc
### Assistant:
VARIABLE: the order of trade items to be entered
CONDITION: should be from lowest to highest
CAUSE_1: The next lower level GTIN must already exist
KEYWORD: therefore
EFFECT_1: the order of trade items to be entered should be from lowest to highest
NOT_RELEVANT: e.g. consumer, then intermediate, then traded, etc.
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The trade item identification that is being permanently replaced must already be in the home data pool in order to ensure data integrity
### Assistant:
VARIABLE: data integrity
KEYWORD: in order
CONDITION: to ensure
CAUSE_1: is being permanently replaced
CAUSE_2: must already be in the home data pool
EFFECT_1: to ensure data integrity
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Must be present to enable data to be presented to trade item catalogue
### Assistant:
CONDITION: to enable
VARIABLE: data to be presented to trade item catalogue
CAUSE_1: Must be present
EFFECT_1: to enable data to be presented to trade item catalogue
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
This field is mandatory within the Global Data Synchronization work process
### Assistant:
VARIABLE: the Global Data Synchronization work process
CONDITION: is mandatory
KEYWORD: within
EFFECT_1: This field is mandatory
CAUSE_1: the Global Data Synchronization work process
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Required if ClassificationCategoryCode field is populated
### Assistant:
CONDITION: is populated
KEYWORD: if
VARIABLE: ClassificationCategoryCode field
EFFECT_1: Required
CAUSE_1: ClassificationCategoryCode field is populated
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Business Rules: If used, the first attribute, which is the primary attribute, is mandatory
### Assistant:
NOT_RELEVANT: Business Rules:
KEYWORD: If
CONDITION: is mandatory
VARIABLE: the first attribute, which is the primary attribute,
CAUSE_1: used
EFFECT_1: the first attribute, which is the primary attribute, is mandatory
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Once an attribute code is selected, it is mandatory to submit an associated value code
### Assistant:
KEYWORD: Once
VARIABLE: it
CONDITION: is mandatory to submit an associated value code
CAUSE_1: an attribute code is selected
EFFECT_1: it is mandatory to submit an associated value code
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The Target Market Subdivision Code describes the "geo-political subdivision of a country" where the trade item is available for sale, as determined by the information provider
### Assistant:
VARIABLE: the trade item
CONDITION: determined by the information provider
KEYWORD: as
EFFECT_1: The Target Market Subdivision Code describes the "geo-political subdivision of a country"
CAUSE_1: the trade item is available for sale
CAUSE_2: determined by the information provider
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
It is important to note that the lack of the Target Market Subdivision code implies that the trade item is available in the entire target market country
### Assistant:
NOT_RELEVANT: It is important to note that
CONDITION: is available in the entire target market country
VARIABLE: the trade item
KEYWORD: implies that
CAUSE_1: the lack of the Target Market Subdivision code
EFFECT_1: the trade item is available in the entire target market country
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
As a descriptive text field, this data element is repeatable for each language used and must be associated with a valid ISO language code from the attached table
### Assistant:
KEYWORD: As
VARIABLE: this data element
CONDITION: must be associated with a valid ISO language code from the attached table
CAUSE_1: a descriptive text field
AND: and
EFFECT_1: this data element is repeatable for each language used
EFFECT_2: must be associated with a valid ISO language code from the attached table
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If a trade item does not have a brand, Use ‘unbranded’ in the description
### Assistant:
KEYWORD: If
VARIABLE: a trade item
CONDITION: Use ‘unbranded’ in the description
CAUSE_1: a trade item does not have a brand
EFFECT_1: Use ‘unbranded’ in the description
NEGATION: does not
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If a trade item is changed from being "Unbranded" to a brand, this requires a new GTIN
### Assistant:
KEYWORD: If
VARIABLE: this
CONDITION: requires a new GTIN
CAUSE_1: a trade item is changed from being "Unbranded" to a brand
EFFECT_1: this requires a new GTIN
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Examples*: When a supplier sends all information in maximum size the For automatic use of this information, e.g. on a tag, a separator should be given between all elements
### Assistant:
NOT_RELEVANT: Examples*:
KEYWORD: When
VARIABLE: a separator
CONDITION: should be given between all elements
CAUSE_1: a supplier sends all information in maximum size the For automatic use of this information, e.g. on a tag,
EFFECT_1: a separator should be given between all elements
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
When implemented, these four attributes may be concatenated as appropriate
### Assistant:
KEYWORD: When
CONDITION: may be concatenated as appropriate
VARIABLE: these four attributes
CAUSE_1: implemented
EFFECT_1: these four attributes may be concatenated as appropriate
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The country code (codes) in which the goods have been produced or manufactured, according to criteria established for the purposes of application of the value may or may not be presented on the trade item label
### Assistant:
VARIABLE: criteria
OR: or
KEYWORD: according to
CONDITION: be presented on the trade item label
NEGATION: not
EFFECT_1: The country code (codes) in which the goods have been produced
EFFECT_2: may
CAUSE_1: criteria established for the purposes of application of the value
EFFECT_3: may not be presented on the trade item label
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
In this case, the buyer will only know the actual country of origin at the time of delivery
### Assistant:
KEYWORD: In
CONDITION: will only know the actual country of origin at the time of delivery
VARIABLE: the buyer
CAUSE_1: this case
EFFECT_1: the buyer will only know the actual country of origin at the time of delivery
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
While the class BrandOwnerOfTradeItem is optional, the party identification for the brand owner is required when a brand owner is present
### Assistant:
KEYWORD: when
VARIABLE: a brand owner
CONDITION: is present
CAUSE_1: the class BrandOwnerOfTradeItem is optional
EFFECT_1: the party identification for the brand owner is required
CAUSE_2: a brand owner is present
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Business Rules: Information required when dangerousGoodsIndicator equals Y
### Assistant:
NOT_RELEVANT: Business Rules:
VARIABLE: dangerousGoodsIndicator
CONDITION: equals Y
KEYWORD: when
EFFECT_1: Information required
CAUSE_1: dangerousGoodsIndicator equals Y
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If they are possible, they must be indicated, whether they are used by the data supplier
### Assistant:
KEYWORD: If
VARIABLE: they
CONDITION: must be indicated, whether they are used by the data supplier
CAUSE_1: they are possible
EFFECT_1: they must be indicated, whether they are used by the data supplier
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Business Rules: Information required when dangerousGoodsIndicator equals Y. Repeatable per dangerousGoodsRegulation code
### Assistant:
NOT_RELEVANT: Repeatable per dangerousGoodsRegulation code
VARIABLE: dangerousGoodsIndicator
CONDITION: equals Y
KEYWORD: when
EFFECT_1: Information required
CAUSE_1: dangerousGoodsIndicator equals Y
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Definition: Dangerous goods hazard ID number, which must be applied to the vehicle, when transporting this trade item (dangerous good) by road or rail, to inform the police, the fire brigade and others in case of an accident about the kind of danger caused by the cargo
### Assistant:
KEYWORD: in case of
VARIABLE: an accident
CONDITION: about the kind of danger caused by the cargo
OR: or
AND: and
NOT_RELEVANT: (dangerous good)
EFFECT_1: to inform the police
CAUSE_1: by road
CAUSE_2: rail
EFFECT_2: the fire brigade
EFFECT_3: about the kind of danger caused by the cargo
CAUSE_3: an accident
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Business Rules: Information required when dangerousGoodsIndicator equals Y. Repeatable per dangerousGoodsRegulationcode
### Assistant:
NOT_RELEVANT: Repeatable per dangerousGoodsRegulationcode
VARIABLE: dangerousGoodsIndicator
CONDITION: equals Y
KEYWORD: when
EFFECT_1: Information required
CAUSE_1: dangerousGoodsIndicator equals Y
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Business Rules: While the class ManufacturerOfTradeItem is optional, the party identification for the manufacturer is required when the manufacturer of trade item is populated
### Assistant:
NOT_RELEVANT: Business Rules:
KEYWORD: when
VARIABLE: the manufacturer of trade item
CONDITION: is populated
CAUSE_1: the class ManufacturerOfTradeItem is optional
EFFECT_1: the party identification for the manufacturer is required
CAUSE_2: the manufacturer of trade item is populated
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
A Dynamic Assortment is when the quantity of the case remains fixed, however the mix of the lower level GTINs may vary from case to case
### Assistant:
VARIABLE: the mix of the lower level GTINs
CONDITION: may vary from case to case
KEYWORD: when
AND: however
EFFECT_1: A Dynamic Assortment is
CAUSE_1: the quantity of the case remains fixed
CAUSE_2: the mix of the lower level GTINs may vary from case to case
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If Next Lower Level Trade Item Information is specified the actual quantity of each child item may be different from pack to pack
### Assistant:
KEYWORD: If
VARIABLE: the actual quantity of each child item
CONDITION: may be different from pack to pack
CAUSE_1: Next Lower Level Trade Item Information is specified
EFFECT_1: the actual quantity of each child item may be different from pack to pack
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Business Rules: Dynamic Assortment may only be specified when the tradeItemUnitDescriptor specifies PREPACK
### Assistant:
NOT_RELEVANT: Business Rules:
VARIABLE: the tradeItemUnitDescriptor
CONDITION: be specified
KEYWORD: only
EFFECT_1: be specified
CAUSE_1: the tradeItemUnitDescriptor specifies PREPACK
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Business Rules: materialCode and materialAgencyCode are paired elements; if one is used the other must be present
### Assistant:
NOT_RELEVANT: Business Rules: materialCode and materialAgencyCode are paired elements;
KEYWORD: if
VARIABLE: the other
CONDITION: must be present
CAUSE_1: one is used
EFFECT_1: the other must be present
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
It is used in conjunction with size group to completely define the size dimension
### Assistant:
VARIABLE: the size dimension
CONDITION: to completely define
CAUSE_1: It is used in conjunction with size group
EFFECT_1: to completely define the size dimension
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
This will allow for the size dimensions to be specified in using different size systems
### Assistant:
VARIABLE: different size systems
KEYWORD: in
CONDITION: using
EFFECT_1: the size dimensions to be specified
CAUSE_1: This
CAUSE_2: using different size systems
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The Retailers require this if the Item is marked as a Consumer Unit
### Assistant:
VARIABLE: the Item
CONDITION: is marked as a Consumer Unit
KEYWORD: if
EFFECT_1: The Retailers require this
CAUSE_1: the Item is marked as a Consumer Unit
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Only values from the enumerated list can be chosen from the UN/CEFACT
### Assistant:
VARIABLE: values
CONDITION: can be chosen from the UN/CEFACT
KEYWORD: Only
CAUSE_1: values from the enumerated list
EFFECT_1: can be chosen from the UN/CEFACT
### END
|