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### Instruction:
Extract the cause and effect entities from the following sentence:
If the Bar Code Type List class (telling which barcodes are on the package) exists, then there is a barcode on the package
### Assistant:
KEYWORD: then
VARIABLE: there
NOT_RELEVANT: (telling which barcodes are on the package)
CONDITION: is a barcode on the package
CAUSE_1: exists
EFFECT_1: there is a barcode on the package
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Business Rules: If the physical dimensions of the product change as a result of the promotion, then a new GTIN must be allocated
### Assistant:
NOT_RELEVANT: Business Rules:
KEYWORD: then
VARIABLE: a new GTIN
CONDITION: must be allocated
CAUSE_1: the physical dimensions of the product change as a result of the promotion
EFFECT_1: a new GTIN must be allocated
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If only a token is added to the product, then no need to change GTIN
### Assistant:
KEYWORD: then
VARIABLE: need to change GTIN
CONDITION: is added to the product
NEGATION: no
CAUSE_1: a token is added to the product
EFFECT_1: no need to change GTIN
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Definition: Used to indicate the peg hole numbers when more than one hole is present in the product or packaging
### Assistant:
NOT_RELEVANT: Definition:
CONDITION: packaging
KEYWORD: when
VARIABLE: more than one hole
OR: or
EFFECT_1: Used to indicate the peg hole numbers
CAUSE_1: more than one hole is present in the product
CAUSE_2: packaging
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Required if the trade item is displayed on a peg board
### Assistant:
CONDITION: is displayed on a peg board
KEYWORD: if
VARIABLE: the trade item
EFFECT_1: Required
CAUSE_1: the trade item is displayed on a peg board
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Business Rules: Required if the item has a peg hole
### Assistant:
NOT_RELEVANT: Business Rules:
CONDITION: has a peg hole
EFFECT_1: Required
KEYWORD: if
VARIABLE: the item
CAUSE_1: the item has a peg hole
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Definition: Used to indicate the horizontal distance from the edge of the trade item to the center of the hole into which the peg is inserted when the trade item is displayed on a pegboard
### Assistant:
KEYWORD: when
VARIABLE: the trade item
CONDITION: Used to indicate the horizontal distance from the edge of the trade item to the center of the hole into which the peg is inserted
NOT_RELEVANT: Definition:
EFFECT_1: Used to indicate the horizontal distance from the edge of the trade item to the center of the hole into which the peg is inserted
CAUSE_1: the trade item is displayed on a pegboard
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Business Rules: Required if the trade item is displayed on a peg board
### Assistant:
NOT_RELEVANT: Business Rules:
CONDITION: is displayed on a peg board
EFFECT_1: Required
KEYWORD: if
VARIABLE: the trade item
CAUSE_1: the trade item is displayed on a peg board
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The hole into which the peg is inserted when the trade item is displayed on a pegboard
### Assistant:
VARIABLE: the trade item
KEYWORD: when
CONDITION: is displayed on a pegboard
EFFECT_1: The hole into which the peg is inserted
CAUSE_1: the trade item is displayed on a pegboard
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Business Rules: As monetary amount field, this data element must be associated with a valid unit of measure
### Assistant:
NOT_RELEVANT: Business Rules:
KEYWORD: As
CONDITION: must be associated with a valid unit of measure
VARIABLE: this data element
CAUSE_1: monetary amount field
EFFECT_1: this data element must be associated with a valid unit of measure
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
GTIN allocation rules state that price is not a relevant criteria for changing a GTIN except when the price is printed as a part of the trade item’s packaging
### Assistant:
VARIABLE: the price
NEGATION: except
CONDITION: is printed as a part of the trade item’s packaging
KEYWORD: when
EFFECT_1: price is not a relevant criteria for changing a GTIN
CAUSE_1: the price is printed as a part of the trade item’s packaging
NOT_RELEVANT: GTIN allocation rules state that
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
In this case, the priceOnPack becomes Global (can not vary per Target Market)
### Assistant:
KEYWORD: In
CONDITION: becomes Global
VARIABLE: the priceOnPack
CAUSE_1: this case
NOT_RELEVANT: (can not vary per Target Market)
EFFECT_1: the priceOnPack becomes Global
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If yes, then must have associated PARTY ID for whom the information is intended
### Assistant:
KEYWORD: then
CONDITION: must have
VARIABLE: associated PARTY ID for whom the information is intended
CAUSE_1: yes
EFFECT_1: must have associated PARTY ID for whom the information is intended
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
While the class ManufacturerOfTradeItem is optional, the party identification for the brand owner is required when a manufacturer is present
### Assistant:
KEYWORD: when
VARIABLE: a manufacturer
CONDITION: is present
CAUSE_1: the class ManufacturerOfTradeItem is optional
EFFECT_1: the party identification for the brand owner is required
CAUSE_2: a manufacturer is present
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
This element applies to cases where the supplier has items in many different seasons or if multiple calendar years were involved
### Assistant:
VARIABLE: This element
CONDITION: applies to cases
OR: or
KEYWORD: where
EFFECT_1: This element applies to cases
CAUSE_1: the supplier has items in many different seasons
CAUSE_2: multiple calendar years were involved
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Business Rules: If color code is indicated, colorCodeListAgency is requires
### Assistant:
NOT_RELEVANT: Business Rules:
KEYWORD: If
VARIABLE: colorCodeListAgency
CONDITION: is requires
CAUSE_1: color code is indicated
EFFECT_1: colorCodeListAgency is requires
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The code list required to identify the color of the trade item
### Assistant:
VARIABLE: the color of the trade item
CONDITION: to identify
CAUSE_1: The code list required
EFFECT_1: to identify the color of the trade item
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Business Rules: If color code is indicated, colorCodeListAgency is required
### Assistant:
NOT_RELEVANT: Business Rules:
KEYWORD: If
VARIABLE: colorCodeListAgency
CONDITION: is required
CAUSE_1: color code is indicated
EFFECT_1: colorCodeListAgency is required
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Business Rules: It only applies to the consumer unit
### Assistant:
NOT_RELEVANT: Business Rules:
VARIABLE: the consumer unit
CONDITION: applies
KEYWORD: only
EFFECT_1: It
CAUSE_1: the consumer unit
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
This date would mark when these changes take effect
### Assistant:
VARIABLE: these changes
CONDITION: take effect
KEYWORD: when
EFFECT_1: This date would mark
CAUSE_1: these changes take effect
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If a new user begins to synchronize data on this GTIN after the original effective date, the effective date = the publication date for the new user
### Assistant:
KEYWORD: If
VARIABLE: the original effective date
CONDITION: = the publication date for the new user
CAUSE_1: a new user begins to synchronize data on this GTIN
CAUSE_2: the original effective date
EFFECT_1: the effective date = the publication date for the new user
NEGATION: after
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If Item is private there can be more than 1 value per TM
### Assistant:
KEYWORD: If
VARIABLE: there
CONDITION: can be more than 1 value per TM
CAUSE_1: Item is private
EFFECT_1: there can be more than 1 value per TM
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
In the case of seasonal trade items, only one start/end cycle can be in the data set version being synchronized
### Assistant:
KEYWORD: In the case of
CONDITION: can be in the data set version being synchronized
VARIABLE: end cycle
CAUSE_1: seasonal trade items
OR: /
EFFECT_1: only one start
EFFECT_2: end cycle can be in the data set version being synchronized
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Field is optional since most trade items are offered in an open ended environment
### Assistant:
VARIABLE: most trade items
CONDITION: are offered in an open ended environment
KEYWORD: since
EFFECT_1: Field is optional
CAUSE_1: most trade items are offered in an open ended environment
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If a private trade item, the date can be trading partner dependent, if public, it is a trading partner neutral field
### Assistant:
KEYWORD: if
VARIABLE: it
CONDITION: is a trading partner neutral field
CAUSE_1: a private trade item
EFFECT_1: it is a trading partner neutral field
CAUSE_2: public
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Business Rules: If the trade item is public only one date (CCYY-MM- DDTHH:MM:SS) is valid per GTIN, GLN, TM combination
### Assistant:
NOT_RELEVANT: (CCYY-MM- DDTHH:MM:SS)
KEYWORD: If
VARIABLE: only one date
CONDITION: is valid per GTIN, GLN, TM combination
CAUSE_1: the trade item is public
EFFECT_1: is valid per GTIN, GLN, TM combination
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
This date can be trading partner dependent if the trade item or service is defined as private
### Assistant:
VARIABLE: service
CONDITION: is defined as private
KEYWORD: if
OR: or
EFFECT_1: This date can be trading partner dependent
CAUSE_1: the trade item
CAUSE_2: service is defined as private
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
It is trading partner neutral if the trade item/service is public
### Assistant:
VARIABLE: service
CONDITION: is public
KEYWORD: if
OR: /
EFFECT_1: It is trading partner neutral
CAUSE_1: the trade item
CAUSE_2: service is public
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Item Name: dangerousGoodsIndicator—This boolean question is answered by the application of the class and is thus not included in model or schema
### Assistant:
VARIABLE: This boolean question
CONDITION: included in model or schema
AND: and
KEYWORD: thus
NEGATION: not
NOT_RELEVANT: Item Name: dangerousGoodsIndicator—
CAUSE_1: This boolean question is answered by the application of the class
EFFECT_1: not included in model or schema
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Business Rules: Only used if the pallet has no GTIN
### Assistant:
NOT_RELEVANT: Business Rules:
CONDITION: GTIN
KEYWORD: if
VARIABLE: the pallet
NEGATION: no
EFFECT_1: Only used
CAUSE_1: the pallet has no GTIN
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
In this case it recommended to use GTIN for Pallet identification
### Assistant:
KEYWORD: for
CONDITION: recommended to use GTIN
VARIABLE: Pallet identification
CAUSE_1: this case
EFFECT_1: it recommended to use GTIN
CAUSE_2: Pallet identification
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Business Rules: An inner pack can only contain the same GTIN, and inner pack sizing must be constant
### Assistant:
NOT_RELEVANT: Business Rules:
VARIABLE: inner pack sizing
CONDITION: must be constant
CAUSE_1: An inner pack
AND: and
EFFECT_1: can only contain the same GTIN
EFFECT_2: inner pack sizing must be constant
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
In case of variable quantity trade items, indicates the average quantity
### Assistant:
KEYWORD: In case of
CONDITION: indicates
VARIABLE: the average quantity
CAUSE_1: variable quantity trade items
EFFECT_1: indicates the average quantity
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Business Rules: For consumer unit, Net Content replaces Net Weight (can then be weight, size, volume)
### Assistant:
NOT_RELEVANT: (can then be weight, size, volume)
KEYWORD: For
VARIABLE: Net Content
CONDITION: replaces Net Weight
CAUSE_1: consumer unit
EFFECT_1: Net Content replaces Net Weight
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Applicable for concentrated products and products where the comparison price is calculated based on a measurement other than netContent
### Assistant:
CONDITION: is calculated based on a measurement other than netContent
AND: and
KEYWORD: where
VARIABLE: the comparison price
EFFECT_1: Applicable for concentrated products
EFFECT_2: products
CAUSE_1: the comparison price is calculated based on a measurement other than netContent
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
This information is used in the space process, especially when allocating whole Trade Units (intermediate) into the shelf
### Assistant:
VARIABLE: whole Trade Units (intermediate)
CONDITION: into the shelf
NOT_RELEVANT: especially
KEYWORD: when
CAUSE_1: allocating whole Trade Units (intermediate) into the shelf
EFFECT_1: This information is used in the space process
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If the Order Quantity Minimum is 100, and the Order Quantity Multiple is 20, then the trade item can only be ordered in quantities which are divisible by the Order Quantity Multiple of 20
### Assistant:
KEYWORD: then
VARIABLE: the trade item
CONDITION: can only be ordered in quantities which are divisible by the Order Quantity Multiple of 20
CAUSE_1: the Order Quantity Minimum is 100
AND: and
CAUSE_2: the Order Quantity Multiple is 20
EFFECT_1: the trade item can only be ordered in quantities which are divisible by the Order Quantity Multiple of 20
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If the dispatch unit is delivered on a pallet the pallet type must be given here
### Assistant:
KEYWORD: If
VARIABLE: the pallet type
CONDITION: must be given here
CAUSE_1: the dispatch unit is delivered on a pallet
EFFECT_1: the pallet type must be given here
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
This start date is mandatory and, if no end date is communicated, then implies that the price is effective until further notice
### Assistant:
KEYWORD: then
NEGATION: no
VARIABLE: This start date
CONDITION: implies that the price is effective until further notice
NOT_RELEVANT: is mandatory and
EFFECT_1: implies that the price is effective until further notice
CAUSE_1: no end date is communicated
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
May be used as a guideline by the retailer to establish their actual retail price
### Assistant:
CONDITION: to establish
VARIABLE: their actual retail price
CAUSE_1: May be used as a guideline by the retailer
EFFECT_1: to establish their actual retail price
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Changes in this value do not require a change in GTIN
### Assistant:
VARIABLE: a change in GTIN
NEGATION: do not
CONDITION: require
CAUSE_1: Changes in this value
EFFECT_1: do not require a change in GTIN
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
When the suggestedRetailPrice is provided for information (and not on the package : priceOnPackIndicator = 'N') , it can be different per Target Considerations and Concerns for more details
### Assistant:
NOT_RELEVANT: (and not on the package : priceOnPackIndicator = 'N')
KEYWORD: When
VARIABLE: it
CONDITION: Concerns for more details
CAUSE_1: the suggestedRetailPrice is provided for information
AND: and
EFFECT_1: it can be different per Target Considerations
EFFECT_2: Concerns for more details
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The value from a industry specific code list required to identify the size of the trade item
### Assistant:
VARIABLE: the size of the trade item
CONDITION: to identify
CAUSE_1: The value from a industry specific code list required
EFFECT_1: to identify the size of the trade item
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Business Rules: When no other information is provided (temperatures to market or/and distribution center), this value applies at any point of the supply chain
### Assistant:
NOT_RELEVANT: (temperatures to market or/and distribution center)
KEYWORD: When
NEGATION: no
VARIABLE: this value
CONDITION: applies at any point of the supply chain
CAUSE_1: no other information is provided
EFFECT_1: this value applies at any point of the supply chain
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
For retail, this trade item will be scanned at point of sale
### Assistant:
KEYWORD: For
VARIABLE: this trade item
CONDITION: will be scanned at point of sale
CAUSE_1: retail
EFFECT_1: this trade item will be scanned at point of sale
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Business Rules: As monetary amount field, this data element is must be associated with a valid unit of measure
### Assistant:
NOT_RELEVANT: Business Rules:
KEYWORD: As
CONDITION: is must be associated with a valid unit of measure
VARIABLE: this data element
CAUSE_1: monetary amount field
EFFECT_1: this data element is must be associated with a valid unit of measure
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Should only be submitted if a claim is made on the trade item
### Assistant:
CONDITION: is made on the trade item
KEYWORD: if
VARIABLE: a claim
EFFECT_1: Should only be submitted
CAUSE_1: a claim is made on the trade item
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Core – these attributes are absolutely necessary to facilitate the accurate sharing of data sets between parties of all industries
### Assistant:
VARIABLE: the accurate sharing of data sets between parties of all industries
CONDITION: to facilitate
NOT_RELEVANT: Core –
CAUSE_1: these attributes are absolutely necessary
EFFECT_1: to facilitate the accurate sharing of data sets between parties of all industries
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Where industries use an attribute in different ways, a new attribute must be developed
### Assistant:
KEYWORD: Where
VARIABLE: a new attribute
CONDITION: must be developed
CAUSE_1: industries use an attribute in different ways
EFFECT_1: a new attribute must be developed
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Within the global data synchronization environment, the combination of three key attributes, GLN of information provider, GTIN of the trade item, and the Target Market identifies a unique set of values for the trade items’ attributes
### Assistant:
KEYWORD: Within
VARIABLE: the combination of three key attributes, GLN of information provider, GTIN of the trade item, and the Target Market
CONDITION: identifies a unique set of values for the trade items’ attributes
CAUSE_1: the global data synchronization environment
EFFECT_1: the combination of three key attributes, GLN of information provider, GTIN of the trade item, and the Target Market identifies a unique set of values for the trade items’ attributes
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
As stated above, in addition possible variations in values for the same attribute, the list of attributes communicated may change based on these three keys
### Assistant:
KEYWORD: As
CONDITION: may change based on these three keys
NOT_RELEVANT: in addition possible variations in values for the same attribute
VARIABLE: the list of attributes communicated
CAUSE_1: stated above
EFFECT_1: the list of attributes communicated may change based on these three keys
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Parties that do not operate in these countries will not be required to communicate these attributes to their customers
### Assistant:
VARIABLE: these attributes
KEYWORD: that
NEGATION: do not
CONDITION: to their customers
CAUSE_1: do not operate in these countries
EFFECT_1: will not be required to communicate these attributes to their customers
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
At the time this document was being finalized, this debate was still taking place
### Assistant:
KEYWORD: At the time
VARIABLE: this debate
CONDITION: was still taking place
CAUSE_1: this document was being finalized
EFFECT_1: this debate was still taking place
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
A valid NRF color code will be assigned to the GTIN when all trade items are of the same color in the prepack; otherwise, the NRF color code 999- assorted prepacks whether the prepack is assorted by size only, color only, or both size and color
### Assistant:
VARIABLE: the NRF color code 999- assorted
CONDITION: prepacks whether the prepack is assorted by size only, color only, or both size and color
KEYWORD: when
NEGATION: otherwise
EFFECT_1: A valid NRF color code will be assigned to the GTIN
CAUSE_1: all trade items are of the same color in the prepack
EFFECT_2: the NRF color code 999- assorted prepacks whether the prepack is assorted by size only, color only, or both size and color
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
A valid NRF color code will be assigned to the setpack GTIN when all trade items are of the same color in the setpack, otherwise the NRF color code 999-assorted is assigned
### Assistant:
KEYWORD: when
VARIABLE: the NRF color code 999-assorted
CONDITION: is assigned
EFFECT_1: A valid NRF color code will be assigned to the setpack GTIN
CAUSE_1: all trade items are of the same color in the setpack
NEGATION: otherwise
EFFECT_2: the NRF color code 999-assorted is assigned
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Different setpacks are assigned different GTINs when either the trade item or quantity contents are different
### Assistant:
VARIABLE: quantity contents
CONDITION: are different
KEYWORD: when
OR: or
EFFECT_1: Different setpacks are assigned different GTINs
CAUSE_1: the trade item
CAUSE_2: quantity contents are different
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
All components of each potential level of packaging must already be defined in order to be used in Item Containment
### Assistant:
VARIABLE: Item Containment
CONDITION: to be used in
KEYWORD: in order
CAUSE_1: All components of each potential level of packaging must already be defined
EFFECT_1: to be used in Item Containment
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
These potential modifications will be considered after the EAN.UCC's final agreement on a global classification schema is announced
### Assistant:
VARIABLE: the EAN.UCC's final agreement on a global classification schema
CONDITION: is announced
KEYWORD: after
EFFECT_1: These potential modifications will be considered
CAUSE_1: the EAN.UCC's final agreement on a global classification schema is announced
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If an attribute is left blank or not defined, the product description exists only of attributes populated
### Assistant:
KEYWORD: If
VARIABLE: the product description
CONDITION: exists only of attributes populated
OR: or
NEGATION: not
CAUSE_1: an attribute is left blank
CAUSE_2: not defined
EFFECT_1: the product description exists only of attributes populated
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
For more accurate product description, several new attributes have been added to the model
### Assistant:
KEYWORD: For
CONDITION: have been added to the model
VARIABLE: several new attributes
CAUSE_1: more accurate product description
EFFECT_1: several new attributes have been added to the model
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
It is envisioned these code list will be added to as new requirements are introduced
### Assistant:
VARIABLE: new requirements
CONDITION: are introduced
KEYWORD: as
EFFECT_1: It is envisioned these code list will be added to
CAUSE_1: new requirements are introduced
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
In order to provide the best value to the EANUCC community the UOM code list will be maintained by EANUCC for its community
### Assistant:
KEYWORD: In order
CONDITION: will be maintained by EANUCC for its community
VARIABLE: the UOM code list
EFFECT_1: to provide the best value to the EANUCC community
CAUSE_1: the UOM code list will be maintained by EANUCC for its community
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
When the business requirements are defined additional code values can be added
### Assistant:
KEYWORD: When
VARIABLE: additional code values
CONDITION: can be added
CAUSE_1: the business requirements are defined
EFFECT_1: additional code values can be added
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
A message detailing information about a trade item must include at least one consumable and one orderable unit
### Assistant:
VARIABLE: at least one consumable
CONDITION: must include
AND: and
CAUSE_1: A message detailing information about a trade item
EFFECT_1: must include at least one consumable
EFFECT_2: one orderable unit
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Based on the Trade Item Implementation feedback presentation on July 7, 2004, the following modifications will be made to the Trade Item
### Assistant:
VARIABLE: the following modifications
KEYWORD: Based on
CONDITION: will be made to the Trade Item
EFFECT_1: the following modifications will be made to the Trade Item
CAUSE_1: the Trade Item Implementation feedback presentation on July 7, 2004
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The chief judge’s approval shall be required prior to the purchasing or upgrading of any system
### Assistant:
CAUSE_1: the purchasing
CAUSE_2: upgrading of any system
OR: or
EFFECT_1: The chief judge’s approval shall be required
VARIABLE: any system
CONDITION: upgrading of
NEGATION: prior to
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
When a document is being displayed, the court shall have the option to print one or more pages at once
### Assistant:
CAUSE_1: a document is being displayed
EFFECT_1: the court shall have the option to print one
EFFECT_2: more pages at once
OR: or
KEYWORD: When
VARIABLE: the court
CONDITION: more pages at once
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The new system will replace the current SharePoint-based system and the data held there will be migrated across to the new platform
### Assistant:
EFFECT_1: will be migrated across to the new platform
VARIABLE: the data
CONDITION: held there
NOT_RELEVANT: The new system will replace the current SharePoint-based system and
CAUSE_1: the data held there
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Cases where the maximum available sentence has been imposed by the regulator are closed by the administrator
### Assistant:
EFFECT_1: Cases
CONDITION: has been imposed by the regulator
KEYWORD: where
VARIABLE: the maximum available sentence
CAUSE_1: the maximum available sentence has been imposed by the regulator
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If further information is requested from a regulator, the regulator uploads the documents into a portal
### Assistant:
CAUSE_1: further information is requested from a regulator
EFFECT_1: the regulator uploads the documents into a portal
KEYWORD: If
VARIABLE: the regulator
CONDITION: uploads the documents into a portal
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
When the requested further information is received, a detailed case review is undertaken by the PSA’s internal solicitors and appended to the case as a document
### Assistant:
CAUSE_1: the requested further information is received
EFFECT_1: a detailed case review is undertaken by the PSA’s internal solicitors
AND: and
EFFECT_2: appended to the case as a document
KEYWORD: When
VARIABLE: a detailed case review
CONDITION: appended to the case as a document
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If the case is already a draft case, it is made a full case
### Assistant:
CAUSE_1: the case is already a draft case
EFFECT_1: it is made a full case
KEYWORD: If
VARIABLE: it
CONDITION: is made a full case
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If it relates to an existing case, it is added as a review case from that case, otherwise it is added as a new case
### Assistant:
CAUSE_1: it relates to an existing case
EFFECT_1: it is added as a review case from that case
EFFECT_2: it is added as a new case
NEGATION: otherwise
KEYWORD: If
VARIABLE: it
CONDITION: is added as a new case
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If the reviewer requires a second opinion before the case is closed, the ‘Second check requested’ field is ticked
### Assistant:
CAUSE_1: the reviewer requires a second opinion
EFFECT_1: the ‘Second check requested’ field is ticked
KEYWORD: If
VARIABLE: the case
CONDITION: is closed
NEGATION: before
CAUSE_2: the case is closed
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
When all information is received, the ‘All information received’ box is ticked in the regulator portal
### Assistant:
CAUSE_1: all information is received
EFFECT_1: the ‘All information received’ box is ticked in the regulator portal
VARIABLE: all information
CONDITION: is received
KEYWORD: When
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The person undertaking the DCR adds their name as the caseworker and changes the stage to ‘Detailed case review’
### Assistant:
CAUSE_1: The person undertaking the DCR
EFFECT_1: adds their name as the caseworker
EFFECT_2: changes the stage to ‘Detailed case review’
AND: and
VARIABLE: The person
CONDITION: changes the stage to ‘Detailed case review’
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Once the review has been drafted, it’s added as a document to the case and also saved to the S Drive
### Assistant:
CAUSE_1: the review has been drafted
EFFECT_1: it’s added as a document to the case
AND: and
EFFECT_2: also saved to the S Drive
KEYWORD: Once
VARIABLE: it
CONDITION: also saved to the S Drive
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Once the review has been drafted, it’s added as a document to the case and saved to the S Drive
### Assistant:
CAUSE_1: the review has been drafted
EFFECT_1: it’s added as a document to the case
EFFECT_2: saved to the S Drive
AND: and
VARIABLE: the review
KEYWORD: Once
CONDITION: saved to the S Drive
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Where a case is a review of an existing case, it should be possible to create the review case from the parent substantive case
### Assistant:
CAUSE_1: a case is a review of an existing case
EFFECT_1: it should be possible to create the review case from the parent substantive case
VARIABLE: a case
CONDITION: is a review of an existing case
KEYWORD: Where
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
When a case is added, the deadline date for the PSA to appeal a decision should be automatically populated
### Assistant:
CAUSE_1: a case is added
EFFECT_1: the deadline date for the PSA to appeal a decision should be automatically populated
KEYWORD: When
VARIABLE: the deadline date for the PSA to appeal a decision
CONDITION: should be automatically populated
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
For example, limited to ‘Doctor’ if the GMC was selected as the regulator
### Assistant:
NOT_RELEVANT: For example
CAUSE_1: the GMC was selected as the regulator
EFFECT_1: limited to ‘Doctor’
CONDITION: was selected as the regulator
VARIABLE: the GMC
KEYWORD: if
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
It should be possible to stop a user moving from one stage to another if defined fields haven’t been filled out, or specific documents uploaded
### Assistant:
CAUSE_1: defined fields haven’t been filled out
CAUSE_2: specific documents uploaded
EFFECT_1: It should be possible to stop a user moving from one stage to another
VARIABLE: It
NEGATION: haven’t
CONDITION: should be possible to stop a user moving from one stage to another
KEYWORD: if
OR: or
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
In order to make the number of fields on the case entity manageable, the system should ideally allow unused sections of data to be hidden on the entity form if they aren’t populated
### Assistant:
CAUSE_1: they aren’t populated
VARIABLE: the number of fields on the case entity
NEGATION: aren’t
CONDITION: manageable
KEYWORD: In order
EFFECT_2: the system should ideally allow unused sections of data to be hidden on the entity form
EFFECT_1: to make the number of fields on the case entity manageable
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
It should not be possible to save a case with a ‘Stage’ field value of ‘Send learning point’, if there is no learning point linked to the case
### Assistant:
CAUSE_1: there is no learning point linked to the case
KEYWORD: if
VARIABLE: there
CONDITION: learning point linked to the case
NOT_RELEVANT: It should
EFFECT_1: not be possible to save a case with a ‘Stage’ field value of ‘Send learning point’
NEGATION: not
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
It should be possible for activities scheduled in the Outlook diary to be synchronised with the diary within the CRM system
### Assistant:
CAUSE_1: activities scheduled in the Outlook diary
NOT_RELEVANT: It should be possible
EFFECT_1: to be synchronised with the diary within the CRM system
CONDITION: scheduled in the Outlook diary
VARIABLE: activities
KEYWORD: for
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
When the regulator accesses the portal, they will be presented with the cases where they have been requested to provide further information, but where that upload has yet to be completed
### Assistant:
CAUSE_1: the regulator accesses the portal
CAUSE_2: that upload has yet to be completed
EFFECT_1: they will be presented with the cases where they have been requested to provide further information
KEYWORD: where
VARIABLE: that upload
CONDITION: has yet to be completed
AND: but
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The PSA administrator should receive an automated email notification when files have been uploaded to the portal
### Assistant:
CAUSE_1: files have been uploaded to the portal
EFFECT_1: The PSA administrator should receive an automated email notification
KEYWORD: when
VARIABLE: files
CONDITION: have been uploaded to the portal
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Once the PSA administrator indicates all documents have been received by changing the stage to ‘All information received’ the case folder will no longer be visible to the regulator
### Assistant:
CAUSE_2: by changing the stage to ‘All information received’
EFFECT_1: the case folder will no longer be visible to the regulator
KEYWORD: Once
NEGATION: will no
VARIABLE: the case folder
CONDITION: longer be visible to the regulator
CAUSE_1: the PSA administrator indicates all documents have been received
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The PSA will be able to ‘unshare’ a case at which point it will no longer be visible to the legal firm
### Assistant:
CAUSE_1: The PSA will be able to ‘unshare’ a case
EFFECT_1: it will no longer be visible to the legal firm
KEYWORD: at which point
VARIABLE: it
CONDITION: longer be visible to the legal firm
NEGATION: will no
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
It should be possible for a user to preview a mail-merge document before printing or emailing and edit the merged document if required
### Assistant:
EFFECT_1: a user to preview a mail-merge document
CAUSE_1: printing
CAUSE_2: emailing
CAUSE_3: edit the merged document
NEGATION: before
AND: and
OR: or
NOT_RELEVANT: if required
KEYWORD: for
VARIABLE: the merged document
CONDITION: edit
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
When a mail-merge is completed, the user should have the ability to save details of the mail-merge to contact history
### Assistant:
CAUSE_1: a mail-merge is completed
EFFECT_1: the user should have the ability to save details of the mail-merge to contact history
KEYWORD: When
VARIABLE: the user
CONDITION: should have the ability to save details of the mail-merge to contact history
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
Because these details differ for each relationship, they are not defined in this project’s context
### Assistant:
CAUSE_1: these details differ for each relationship
EFFECT_1: they are not defined in this project’s context
NEGATION: are not
VARIABLE: these details
CONDITION: differ for each relationship
KEYWORD: Because
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The user can discard the information or react to it, where applicable (e.g. open a link, jump into a specific function)1
### Assistant:
CAUSE_1: applicable
EFFECT_1: The user can discard the information
EFFECT_2: react to it
VARIABLE: The user
CONDITION: applicable
KEYWORD: where
OR: or
NOT_RELEVANT: (e.g. open a link, jump into a specific function)1
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The Airline package contains those use cases that concern the airline and passenger bag information
### Assistant:
CAUSE_1: concern the airline
CAUSE_2: passenger bag information
CONDITION: contains those use cases
AND: and
EFFECT_1: The Airline package contains those use cases
VARIABLE: The Airline package
KEYWORD: that
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
A partner requests information about a baggage status, where the piece of luggage is identified by a unique identifier
### Assistant:
CAUSE_1: the piece of luggage is identified by a unique identifier
EFFECT_1: A partner requests information about a baggage status
KEYWORD: where
VARIABLE: A partner
CONDITION: requests information about a baggage status
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
The application provides the user with a list of all services which can be used
### Assistant:
CAUSE_1: can be used
KEYWORD: which
EFFECT_1: The application provides the user with a list of all services
VARIABLE: The application
CONDITION: can be used
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
To make it easy to find a service the user can search via a single search field
### Assistant:
EFFECT_1: easy to find a service
CAUSE_1: the user can search via a single search field
KEYWORD: To make it
VARIABLE: a service
CONDITION: easy to find
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If the user hasn’t selected a home airport, the first step is that the application asks the user which Airport should be used
### Assistant:
CAUSE_1: the user hasn’t selected a home airport
EFFECT_1: the application asks the user which Airport should be used
KEYWORD: If
NOT_RELEVANT: the first step is that
VARIABLE: the application
NEGATION: hasn’t
CONDITION: asks the user which Airport should be used
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If the user hasn’t selected a home airport, the first step is that the application asks the user which Airport should be used for searching flights
### Assistant:
CAUSE_1: the user hasn’t selected a home airport
EFFECT_1: the application asks the user which Airport should be used for searching flights
KEYWORD: If
NOT_RELEVANT: the first step is that
VARIABLE: the application
NEGATION: hasn’t
CONDITION: asks the user which Airport should be used for searching flights
### END
|
### Instruction:
Extract the cause and effect entities from the following sentence:
If the user hasn’t selected a default airport, the first step is that the application asks the user which Airport should be displayed
### Assistant:
CAUSE_1: the user hasn’t selected a default airport
EFFECT_1: the application asks the user which Airport should be displayed
KEYWORD: If
NOT_RELEVANT: the first step is that
VARIABLE: the application
NEGATION: hasn’t
CONDITION: asks the user which Airport should be displayed
### END
|