A computer-implemented method generates a temporal candidate answer to a question. One or more processors tag terms found in multiple text passages with a part of speech (POS) tag, and then parse POS tagged terms found in the multiple text passages to generate passage triples for multiple parts of each of the multiple text passages. The processor(s) filter each sub-passage from the multiple text passages, which removes passages from each of the multiple text passages that are not associated with said each sub-passage. The processor(s) temporally align each sub-passage triple with a particular time range, and then extract text within each sub-passage from the multiple text passages to create a temporal candidate answer identified by the associated sub-passage triple. Upon receipt of a question related to a sub-passage that is related to the particular time range, the processor(s) return the temporal candidate answer that is temporally aligned with the question.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: tagging, by one or more processors, terms found in multiple text passages with a part of speech (POS) tag; parsing, by one or more processors, POS tagged terms found in the multiple text passages to generate passage triples for multiple parts of each of the multiple text passages, wherein each passage triple includes a sub-passage entity, a sub-passage action, and a sub-passage date within each of the multiple text passages, wherein each sub-passage entity identifies an entity in each sub-passage, wherein each sub-passage action describes an action being performed by the entity in each sub-passage, and wherein each sub-passage date defines a time range during which an action was performed by the entity in each sub-passage, and wherein the sub-passage entity, the sub-passage action, and the sub-passage date make up a sub-passage triple; filtering, by one or more processors, each sub-passage from the multiple text passages, wherein said filtering removes passages, from each of the multiple text passages, not associated with said each sub-passage; temporally aligning, by one or more processors, each sub-passage triple with a particular time range; extracting, by one or more processors, text within each sub-passage from the multiple text passages to create a temporal candidate answer identified by the associated sub-passage triple; receiving, by one or more processors, a question related to a sub-passage that is related to the particular time range; and returning, by one or more processors, the temporal candidate answer that is temporally aligned with the question. 2. The computer-implemented method of claim 1, further comprising: weighting, by one or more processors, the temporal candidate answer higher than any other candidate answer from the multiple text passages. 3. The computer-implemented method of claim 1, further comprising: semantically matching, by one or more processors, the temporal candidate answer to the question in order to temporally align the temporal candidate answer to the question. 4. The computer-implemented method of claim 1, further comprising: stem word matching, by one or more processors, the temporal candidate answer to the question in order to temporally align the temporal candidate answer to the question. 5. The computer-implemented method of claim 1, further comprising: synonym matching, by one or more processors, the temporal candidate answer to the question in order to temporally align the temporal candidate answer to the question. 6. The computer-implemented method of claim 1, further comprising: applying, by one or more processors, a lexical answer type analysis of each of the sub-passages in order to define an event that each sub-passage describes, wherein the lexical answer type analysis identifies the sub-passage entity as a person. 7. The computer-implemented method of claim 1, further comprising: applying, by one or more processors, a lexical answer type analysis of each of the sub-passages in order to define an event that each sub-passage describes, wherein the lexical answer type analysis identifies the sub-passage entity as a place. 8. A computer program product comprising one or more computer readable storage mediums, and program instructions stored on at least one of the one or more storage mediums, the stored program instructions comprising: program instructions to tag terms found in multiple text passages with a part of speech (POS) tag; program instructions to parse POS tagged terms in the multiple text passages to generate passage triples for multiple parts of each of the multiple text passages, wherein each passage triple includes a sub-passage entity, a sub-passage action, and a sub-passage date within each of the multiple text passages, wherein each sub-passage entity identifies an entity in each sub-passage, wherein each sub-passage action describes an action being performed by the entity in each sub-passage, and wherein each sub-passage date defines a time range during which an action was performed by the entity in each sub-passage, and wherein the sub-passage entity, the sub-passage action, and the sub-passage date make up a sub-passage triple; program instructions to filter each sub-passage from the multiple text passages, wherein said filtering removes passages, from each of the multiple text passages, not associated with said each sub-passage; program instructions to temporally align each sub-passage triple with a particular time range; program instructions to extract text within each sub-passage from the multiple text passages to create a temporal candidate answer identified by the associated sub-passage triple; program instructions to receive a question related to a sub-passage that is related to the particular time range; and program instructions to return the temporal candidate answer that is temporally aligned with the question. 9. The computer program product of claim 8, further comprising: program instructions to weight the temporal candidate answer higher than any other candidate answer from the multiple text passages. 10. The computer program product of claim 8, further comprising: program instructions to semantically match the temporal candidate answer to the question in order to temporally align the temporal candidate answer to the question. 11. The computer program product of claim 8, further comprising: program instructions to stem word match the temporal candidate answer to the question in order to temporally align the temporal candidate answer to the question. 12. The computer program product of claim 8, further comprising: program instructions to synonym match the temporal candidate answer to the question in order to temporally align the temporal candidate answer to the question. 13. The computer program product of claim 8, further comprising: program instructions to apply a lexical answer type analysis of each of the sub-passages in order to define an event that each sub-passage describes, wherein the lexical answer type analysis identifies the sub-passage entity as a person. 14. The computer program product of claim 8, further comprising: program instructions to apply a lexical answer type analysis of each of the sub-passages in order to define an event that each sub-passage describes, wherein the lexical answer type analysis identifies the sub-passage entity as a place. 15. The computer program product of claim 8, wherein the program instructions are provided as a service in a cloud environment. 16. A computer system comprising one or more processors, one or more computer readable memories, and one or more computer readable storage mediums, and program instructions stored on at least one of the one or more storage mediums for execution by at least one of the one or more processors via at least one of the one or more memories, the stored program instructions comprising: program instructions to tag terms found in multiple text passages with a part of speech (POS) tag; program instructions to parse POS tagged terms in the multiple text passages to generate passage triples for multiple parts of each of the multiple text passages, wherein each passage triple includes a sub-passage entity, a sub-passage action, and a sub-passage date within each of the multiple text passages, wherein each sub-passage entity identifies an entity in each sub-passage, wherein each sub-passage action describes an action being performed by the entity in each sub-passage, and wherein each sub-passage date defines a time range during which an action was performed by the entity in each sub-passage, and wherein the sub-passage entity, the sub-passage action, and the sub-passage date make up a sub-passage triple; program instructions to filter each sub-passage from the multiple text passages, wherein said filtering removes passages, from each of the multiple text passages, not associated with said each sub-passage; program instructions to temporally align each sub-passage triple with a particular time range; program instructions to extract text within each sub-passage from the multiple text passages to create a temporal candidate answer identified by the associated sub-passage triple; program instructions to receive a question related to a sub-passage that is related to the particular time range; and program instructions to return the temporal candidate answer that is temporally aligned with the question. 17. The computer system of claim 16, further comprising: program instructions to weight the temporal candidate answer higher than any other candidate answer from the multiple text passages. 18. The computer system of claim 16, further comprising: program instructions to semantically match the temporal candidate answer to the question in order to temporally align the temporal candidate answer to the question. 19. The computer system of claim 16, further comprising: program instructions to apply a lexical answer type analysis of each of the sub-passages in order to define an event that each sub-passage describes, wherein the lexical answer type analysis identifies the sub-passage entity as a person. 20. The computer system of claim 16, further comprising: program instructions to apply a lexical answer type analysis of each of the sub-passages in order to define an event that each sub-passage describes, wherein the lexical answer type analysis identifies the sub-passage entity as a place.