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‎specification/cognitiveservices/data-plane/LanguageAnalyzeText/examples/2026-05-01/SuccessfulAbstractiveSummarizationSummaryLengthTaskResult.json‎

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{
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"operationId": "AnalyzeTextJobStatus",
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"title": "SuccessfulAbstractiveSummarizationSummaryLengthTaskResult",
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"parameters": {
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"Ocp-Apim-Subscription-Key": "{API key}",
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"api-version": "2026-05-01",
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"Endpoint": "{Endpoint}",
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"jobId": "c0f2a446-05d9-48fc-ba8f-3ef4af8d0b18"
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},
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"responses": {
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"200": {
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"headers": {},
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"body": {
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"createdDateTime": "2020-10-01T15:00:45Z",
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"displayName": "Document Abstractive Summarization Task Example",
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"expirationDateTime": "2020-10-03T15:01:03Z",
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"jobId": "c0f2a446-05d9-48fc-ba8f-3ef4af8d0b18",
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"lastUpdatedDateTime": "2020-10-01T15:01:03Z",
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"status": "succeeded",
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"tasks": {
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"completed": 1,
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"failed": 0,
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"inProgress": 0,
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"total": 1,
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"items": [
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{
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"kind": "AbstractiveSummarizationLROResults",
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"taskName": "Document Abstractive Summarization Task 1",
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"lastUpdateDateTime": "2020-10-01T15:01:03Z",
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"status": "succeeded",
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"results": {
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"documents": [
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{
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"summaries": [
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{
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"text": "Microsoft is working towards advancing AI through a holistic, human-centric approach. The Chief Technology Officer of Azure AI services, who has a unique perspective on human cognition, is working with a team to develop a joint representation called XYZ-code. This code, which combines monologuistic text, audio or visual sensory signals, and multilinguality, is being used to create more powerful AI that can better understand humans. The goal is to have pre-trained models that can jointly learn representations to support a broad range of AI tasks, similar to how humans do today. Over the past five years, Microsoft has achieved human performance on benchmarks in conversational speech recognition, machine translation, conversational question answering, machine reading comprehension, and image captioning.",
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"contexts": [
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{
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"offset": 0,
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"length": 1629
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}
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]
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}
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],
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"id": "1",
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"warnings": []
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}
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],
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"errors": [],
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"modelVersion": "latest"
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}
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}
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]
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}
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}
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}
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}
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}

‎specification/cognitiveservices/data-plane/LanguageAnalyzeText/examples/2026-05-01/SuccessfulAbstractiveSummaryLengthSummarizationTaskSubmit.json‎

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{
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"operationId": "AnalyzeTextSubmitJob",
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"title": "SuccessfulAbstractiveSummarizationSummaryLengthTaskSubmit",
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"parameters": {
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"Ocp-Apim-Subscription-Key": "{API key}",
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"api-version": "2026-05-01",
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"Endpoint": "{Endpoint}",
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"jobId": "{Job ID}",
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"body": {
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"displayName": "Document Abstractive Summarization Task Example",
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"analysisInput": {
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"documents": [
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{
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"id": "1",
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"language": "en",
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"text": "At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic, human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI Cognitive Services, I have been working with a team of amazing scientists and engineers to turn this quest into a reality. In my role, I enjoy a unique perspective in viewing the relationship among three attributes of human cognition: monolingual text (X), audio or visual sensory signals, (Y) and multilingual (Z). At the intersection of all three, there is magic—what we call XYZ-code as illustrated in Figure 1—a joint representation to create more powerful AI that can speak, hear, see, and understand humans better. We believe XYZ-code will enable us to fulfill our long-term vision: cross-domain transfer learning, spanning modalities and languages. The goal is to have pre-trained models that can jointly learn representations to support a broad range of downstream AI tasks, much in the way humans do today. Over the past five years, we have achieved human performance on benchmarks in conversational speech recognition, machine translation, conversational question answering, machine reading comprehension, and image captioning. These five breakthroughs provided us with strong signals toward our more ambitious aspiration to produce a leap in AI capabilities, achieving multi-sensory and multilingual learning that is closer in line with how humans learn and understand. I believe the joint XYZ-code is a foundational component of this aspiration, if grounded with external knowledge sources in the downstream AI tasks."
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}
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]
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},
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"tasks": [
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{
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"kind": "AbstractiveSummarization",
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"taskName": "Document Abstractive Summarization Task 1",
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"parameters": {
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"summaryLength": "medium"
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}
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}
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]
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}
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},
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"responses": {
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"202": {
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"headers": {
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"Operation-Location": "{endpoint}/language/analyze-text/jobs/{jobId}?api-version=2026-05-01"
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}
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}
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}
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}

specification/cognitiveservices/data-plane/LanguageAnalyzeText/examples/2025-11-15-preview/SuccessfulAbstractiveSummarizationSummaryLengthPromptTaskResult.json renamed to specification/cognitiveservices/data-plane/LanguageAnalyzeText/examples/2026-05-15-preview/SuccessfulAbstractiveSummarizationSummaryLengthPromptTaskResult.json

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"title": "SuccessfulAbstractiveSummarizationSummaryLengthPromptTaskResult",
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"parameters": {
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"Ocp-Apim-Subscription-Key": "{API key}",
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"api-version": "2025-11-15-preview",
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"api-version": "2026-05-15-preview",
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"Endpoint": "{Endpoint}",
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"jobId": "c0f2a446-05d9-48fc-ba8f-3ef4af8d0b18"
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},

specification/cognitiveservices/data-plane/LanguageAnalyzeText/preview/2025-11-15-preview/examples/SuccessfulAbstractiveSummarizationSummaryLengthPromptTaskSubmit.json renamed to specification/cognitiveservices/data-plane/LanguageAnalyzeText/examples/2026-05-15-preview/SuccessfulAbstractiveSummarizationSummaryLengthPromptTaskSubmit.json

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"title": "SuccessfulAbstractiveSummarizationSummaryLengthPromptTaskSubmit",
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"parameters": {
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"Ocp-Apim-Subscription-Key": "{API key}",
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"api-version": "2025-11-15-preview",
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"api-version": "2026-05-15-preview",
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"Endpoint": "{Endpoint}",
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"jobId": "{Job ID}",
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"body": {
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"kind": "AbstractiveSummarization",
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"taskName": "Document Abstractive Summarization Task 1",
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"parameters": {
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"summaryLength": "medium",
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"instruction": "XYZ-code"
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"summaryLength": "medium"
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}
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}
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]
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"responses": {
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"202": {
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"headers": {
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"Operation-Location": "{endpoint}/language/analyze-text/jobs/{jobId}?api-version=2025-11-15-preview"
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"Operation-Location": "{endpoint}/language/analyze-text/jobs/{jobId}?api-version=2026-05-15-preview"
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}
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}
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}

specification/cognitiveservices/data-plane/LanguageAnalyzeText/examples/2025-11-15-preview/SuccessfulAbstractiveSummarizationTaskResult.json renamed to specification/cognitiveservices/data-plane/LanguageAnalyzeText/examples/2026-05-15-preview/SuccessfulAbstractiveSummarizationTaskResult.json

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"title": "SuccessfulAbstractiveSummarizationTaskResult",
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"parameters": {
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"Ocp-Apim-Subscription-Key": "{API key}",
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"api-version": "2025-11-15-preview",
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"api-version": "2026-05-15-preview",
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"Endpoint": "{Endpoint}",
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"jobId": "c0f2a446-05d9-48fc-ba8f-3ef4af8d0b18"
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},

specification/cognitiveservices/data-plane/LanguageAnalyzeText/preview/2025-11-15-preview/examples/SuccessfulAbstractiveSummarizationTaskSubmit.json renamed to specification/cognitiveservices/data-plane/LanguageAnalyzeText/examples/2026-05-15-preview/SuccessfulAbstractiveSummarizationTaskSubmit.json

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"title": "SuccessfulAbstractiveSummarizationTaskSubmit",
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"parameters": {
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"Ocp-Apim-Subscription-Key": "{API key}",
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"api-version": "2025-11-15-preview",
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"api-version": "2026-05-15-preview",
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"Endpoint": "{Endpoint}",
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"jobId": "{Job ID}",
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"body": {
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"responses": {
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"202": {
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"headers": {
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"Operation-Location": "{endpoint}/language/analyze-text/jobs/{jobId}?api-version=2025-11-15-preview"
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"Operation-Location": "{endpoint}/language/analyze-text/jobs/{jobId}?api-version=2026-05-15-preview"
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}
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}
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}

specification/cognitiveservices/data-plane/LanguageAnalyzeText/examples/2025-11-15-preview/SuccessfulAnalyzeTextJobsCancelRequest.json renamed to specification/cognitiveservices/data-plane/LanguageAnalyzeText/examples/2026-05-15-preview/SuccessfulAnalyzeTextJobsCancelRequest.json

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"title": "SuccessfulAnalyzeTextJobsCancelRequest",
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"parameters": {
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"Ocp-Apim-Subscription-Key": "{API key}",
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"api-version": "2025-11-15-preview",
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"api-version": "2026-05-15-preview",
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"Endpoint": "{Endpoint}",
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"jobId": "c0f2a446-05d9-48fc-ba8f-3ef4af8d0b18"
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},

specification/cognitiveservices/data-plane/LanguageAnalyzeText/examples/2025-11-15-preview/SuccessfulAnalyzeTextJobsMultipleTaskStatusRequest.json renamed to specification/cognitiveservices/data-plane/LanguageAnalyzeText/examples/2026-05-15-preview/SuccessfulAnalyzeTextJobsMultipleTaskStatusRequest.json

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"title": "SuccessfulAnalyzeTextJobsMultipleTaskStatusRequest",
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"parameters": {
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"Ocp-Apim-Subscription-Key": "{API key}",
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"api-version": "2025-11-15-preview",
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"api-version": "2026-05-15-preview",
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"Endpoint": "{Endpoint}",
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"jobId": "c0f2a446-05d9-48fc-ba8f-3ef4af8d0b18"
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},

specification/cognitiveservices/data-plane/LanguageAnalyzeText/examples/2025-11-15-preview/SuccessfulAnalyzeTextJobsMultipleTaskSubmitRequest.json renamed to specification/cognitiveservices/data-plane/LanguageAnalyzeText/examples/2026-05-15-preview/SuccessfulAnalyzeTextJobsMultipleTaskSubmitRequest.json

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"title": "SuccessfulAnalyzeTextJobsMultipleTaskSubmitRequest",
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"parameters": {
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"Ocp-Apim-Subscription-Key": "{API key}",
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"api-version": "2025-11-15-preview",
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"api-version": "2026-05-15-preview",
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"Endpoint": "{Endpoint}",
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"jobId": "{Job ID}",
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"body": {
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"responses": {
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"202": {
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"headers": {
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"Operation-Location": "{endpoint}/language/analyze-text/jobs/{jobId}?api-version=2025-11-15-preview"
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"Operation-Location": "{endpoint}/language/analyze-text/jobs/{jobId}?api-version=2026-05-15-preview"
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}
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}
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}

specification/cognitiveservices/data-plane/LanguageAnalyzeText/preview/2025-11-15-preview/examples/SuccessfulEntityLinkingRequest.json renamed to specification/cognitiveservices/data-plane/LanguageAnalyzeText/examples/2026-05-15-preview/SuccessfulEntityLinkingRequest.json

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"title": "SuccessfulEntityLinkingRequest",
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"parameters": {
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"Ocp-Apim-Subscription-Key": "{API key}",
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"api-version": "2025-11-15-preview",
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"api-version": "2026-05-15-preview",
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"Endpoint": "{Endpoint}",
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"body": {
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"kind": "EntityLinking",

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