Datasets:
Maverick17
/
cira_dataset

Dataset card Data Studio Files Community
1
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265
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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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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
### 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