
-Goal-
Given a text document that is potentially relevant to this activity, first identify all entities needed from the text in order to capture the information and ideas in the text.
Next, report all relationships among the identified entities.

-Steps-
1. Identify all entities. For each identified entity, extract the following information:
- entity_name: Name of the entity, capitalized
- entity_type: Suggest several labels or categories for the entity. The categories should not be specific, but should be as general as possible.
- entity_description: Comprehensive description of the entity's attributes and activities
Format each entity as ("entity"{tuple_delimiter}<entity_name>{tuple_delimiter}<entity_type>{tuple_delimiter}<entity_description>)

2. From the entities identified in step 1, identify all pairs of (source_entity, target_entity) that are *clearly related* to each other.
For each pair of related entities, extract the following information:
- source_entity: name of the source entity, as identified in step 1
- target_entity: name of the target entity, as identified in step 1
- relationship_description: explanation as to why you think the source entity and the target entity are related to each other
- relationship_strength: a numeric score indicating strength of the relationship between the source entity and target entity
Format each relationship as ("relationship"{tuple_delimiter}<source_entity>{tuple_delimiter}<target_entity>{tuple_delimiter}<relationship_description>{tuple_delimiter}<relationship_strength>)

3. Return output in The primary language of the provided text is "English." as a single list of all the entities and relationships identified in steps 1 and 2. Use **{record_delimiter}** as the list delimiter.

4. If you have to translate into The primary language of the provided text is "English.", just translate the descriptions, nothing else!

5. When finished, output {completion_delimiter}.

-Examples-
######################

Example 1:

text:
<<<<<<< HEAD
 he tasks studied in the lab thus far have tended to 
be those for which researchers hypothesized generative AI would 
perform well . This was, in fact, the focus of most of the studies 
presented in the first AI and Productivity report we published  
(Cambon et al. 2023) . Actual information work , however, often 
includes a huge variety of tasks  and much of the unstructured and 
informal work in people’s jobs is not yet directly supported by the 
first-generation of generative AI tools.  Software developer 
workflows , for example,  involve far more than the hands-on coding supported by GitHub Copilot (Meyer et al. 2017). The ability to 
shed light on generative AI's productivity dynamics in the natural 
complexity of entire workflows is a key advantage of field studies 
of generative AI’s productivity impacts , and a major reason we 
hope to see many more field studies emerging in the literature
------------------------
output:
("entity"{tuple_delimiter}LAB{tuple_delimiter}LOCATION, RESEARCH ENVIRONMENT{tuple_delimiter}The lab is where tasks are studied to hypothesize the performance of generative AI)
{record_delimiter}
("entity"{tuple_delimiter}GENERATIVE AI{tuple_delimiter}TECHNOLOGY, TOOL{tuple_delimiter}Generative AI refers to artificial intelligence systems that can generate content, such as text, based on input data)
{record_delimiter}
("entity"{tuple_delimiter}AI AND PRODUCTIVITY REPORT{tuple_delimiter}DOCUMENT, PUBLICATION{tuple_delimiter}A report published by Cambon et al. in 2023 focusing on the performance of generative AI in various tasks)
{record_delimiter}
("entity"{tuple_delimiter}INFORMATION WORK{tuple_delimiter}ACTIVITY, TASK{tuple_delimiter}Information work includes a variety of tasks, often unstructured and informal, that are part of people's jobs)
{record_delimiter}
("entity"{tuple_delimiter}FIRST-GENERATION GENERATIVE AI TOOLS{tuple_delimiter}TECHNOLOGY, TOOL{tuple_delimiter}The initial versions of generative AI tools that support specific tasks but not the full range of unstructured work)
{record_delimiter}
("entity"{tuple_delimiter}SOFTWARE DEVELOPER WORKFLOWS{tuple_delimiter}ACTIVITY, TASK{tuple_delimiter}The comprehensive set of activities involved in software development, beyond just coding)
{record_delimiter}
("entity"{tuple_delimiter}GITHUB COPILOT{tuple_delimiter}TECHNOLOGY, TOOL{tuple_delimiter}A generative AI tool that assists with hands-on coding tasks for software developers)
{record_delimiter}
("entity"{tuple_delimiter}FIELD STUDIES{tuple_delimiter}RESEARCH METHOD, STUDY TYPE{tuple_delimiter}Studies conducted in natural settings to observe the real-world impacts of generative AI on productivity)
{record_delimiter}
("entity"{tuple_delimiter}PRODUCTIVITY DYNAMICS{tuple_delimiter}CONCEPT, PHENOMENON{tuple_delimiter}The various factors and interactions that affect productivity when using generative AI in real-world workflows)
{record_delimiter}
("entity"{tuple_delimiter}LITERATURE{tuple_delimiter}BODY OF WORK, RESEARCH{tuple_delimiter}The collection of academic and professional studies and publications on a given topic)
{record_delimiter}
("relationship"{tuple_delimiter}LAB{tuple_delimiter}GENERATIVE AI{tuple_delimiter}The lab is where tasks are studied to hypothesize the performance of generative AI{tuple_delimiter}7)
{record_delimiter}
("relationship"{tuple_delimiter}AI AND PRODUCTIVITY REPORT{tuple_delimiter}GENERATIVE AI{tuple_delimiter}The report focuses on the performance of generative AI in various tasks{tuple_delimiter}8)
{record_delimiter}
("relationship"{tuple_delimiter}INFORMATION WORK{tuple_delimiter}FIRST-GENERATION GENERATIVE AI TOOLS{tuple_delimiter}First-generation generative AI tools do not yet directly support much of the unstructured and informal information work{tuple_delimiter}6)
{record_delimiter}
("relationship"{tuple_delimiter}SOFTWARE DEVELOPER WORKFLOWS{tuple_delimiter}GITHUB COPILOT{tuple_delimiter}GitHub Copilot supports hands-on coding, which is a part of software developer workflows{tuple_delimiter}9)
{record_delimiter}
("relationship"{tuple_delimiter}FIELD STUDIES{tuple_delimiter}PRODUCTIVITY DYNAMICS{tuple_delimiter}Field studies help shed light on the productivity dynamics of generative AI in real-world settings{tuple_delimiter}8)
{record_delimiter}
("relationship"{tuple_delimiter}FIELD STUDIES{tuple_delimiter}LITERATURE{tuple_delimiter}The hope is to see more field studies emerging in the literature to understand generative AI's productivity impacts{tuple_delimiter}7)
=======
 (2024). The 
Use of Generative Search Engines for Knowledge Work and Complex 
Tasks. arXiv preprint. https://doi.org/10.48550/arXiv.2404.04268 . 
Tankelevitch *, L., Kewenig *, V., Simkute, A., Scott, A. E., Sarkar, A., Sellen, A., & 
Rintel, S. (2024). The Metacognitive Demands and Opportunities of Generative 
AI. In Proceedings of the CHI Conference on Human Factors in Computing 
Systems (pp. 1-24). https://doi.org/10.1145/3613904.3642902 . 
Taraborelli, D.  (2015). The Sum of All Human Knowledge in the Age of Machines: A 
New Research Agenda for Wikimedia. ICWSM -15 Workshop on Wikipedia, a 
Social Pedia: Research Challenges and Opportunities .
------------------------
output:
("entity"{tuple_delimiter}GENERATIVE SEARCH ENGINES{tuple_delimiter}TECHNOLOGY{tuple_delimiter}Generative search engines are AI-driven tools used for knowledge work and complex tasks)
{record_delimiter}
("entity"{tuple_delimiter}KNOWLEDGE WORK{tuple_delimiter}ACTIVITY{tuple_delimiter}Knowledge work involves tasks that require significant cognitive effort and expertise)
{record_delimiter}
("entity"{tuple_delimiter}COMPLEX TASKS{tuple_delimiter}ACTIVITY{tuple_delimiter}Complex tasks are activities that require advanced problem-solving and critical thinking skills)
{record_delimiter}
("entity"{tuple_delimiter}ARXIV{tuple_delimiter}PLATFORM{tuple_delimiter}arXiv is an open-access repository for research papers)
{record_delimiter}
("entity"{tuple_delimiter}CHI CONFERENCE{tuple_delimiter}EVENT{tuple_delimiter}The CHI Conference on Human Factors in Computing Systems is an annual event focusing on human-computer interaction)
{record_delimiter}
("entity"{tuple_delimiter}WIKIMEDIA{tuple_delimiter}ORGANIZATION{tuple_delimiter}Wikimedia is a non-profit organization that supports Wikipedia and other free knowledge projects)
{record_delimiter}
("entity"{tuple_delimiter}TANKELEVITCH, L.{tuple_delimiter}PERSON{tuple_delimiter}L. Tankelevitch is an author of a paper on generative search engines for knowledge work and complex tasks)
{record_delimiter}
("entity"{tuple_delimiter}KEWENIG, V.{tuple_delimiter}PERSON{tuple_delimiter}V. Kewenig is an author of a paper on generative search engines for knowledge work and complex tasks)
{record_delimiter}
("entity"{tuple_delimiter}SIMKUTE, A.{tuple_delimiter}PERSON{tuple_delimiter}A. Simkute is an author of a paper on generative search engines for knowledge work and complex tasks)
{record_delimiter}
("entity"{tuple_delimiter}SCOTT, A. E.{tuple_delimiter}PERSON{tuple_delimiter}A. E. Scott is an author of a paper on generative search engines for knowledge work and complex tasks)
{record_delimiter}
("entity"{tuple_delimiter}SARKAR, A.{tuple_delimiter}PERSON{tuple_delimiter}A. Sarkar is an author of a paper on generative search engines for knowledge work and complex tasks)
{record_delimiter}
("entity"{tuple_delimiter}SELLEN, A.{tuple_delimiter}PERSON{tuple_delimiter}A. Sellen is an author of a paper on generative search engines for knowledge work and complex tasks)
{record_delimiter}
("entity"{tuple_delimiter}RINTEL, S.{tuple_delimiter}PERSON{tuple_delimiter}S. Rintel is an author of a paper on generative search engines for knowledge work and complex tasks)
{record_delimiter}
("entity"{tuple_delimiter}TARABORELLI, D.{tuple_delimiter}PERSON{tuple_delimiter}D. Taraborelli is an author of a paper on the impact of machines on human knowledge)
{record_delimiter}
("entity"{tuple_delimiter}ICWSM{tuple_delimiter}EVENT{tuple_delimiter}ICWSM is a conference focusing on social media research)
{record_delimiter}
("relationship"{tuple_delimiter}GENERATIVE SEARCH ENGINES{tuple_delimiter}KNOWLEDGE WORK{tuple_delimiter}Generative search engines are used for knowledge work{tuple_delimiter}8)
{record_delimiter}
("relationship"{tuple_delimiter}GENERATIVE SEARCH ENGINES{tuple_delimiter}COMPLEX TASKS{tuple_delimiter}Generative search engines are used for complex tasks{tuple_delimiter}8)
{record_delimiter}
("relationship"{tuple_delimiter}TANKELEVITCH, L.{tuple_delimiter}GENERATIVE SEARCH ENGINES{tuple_delimiter}L. Tankelevitch authored a paper on generative search engines{tuple_delimiter}7)
{record_delimiter}
("relationship"{tuple_delimiter}KEWENIG, V.{tuple_delimiter}GENERATIVE SEARCH ENGINES{tuple_delimiter}V. Kewenig authored a paper on generative search engines{tuple_delimiter}7)
{record_delimiter}
("relationship"{tuple_delimiter}SIMKUTE, A.{tuple_delimiter}GENERATIVE SEARCH ENGINES{tuple_delimiter}A. Simkute authored a paper on generative search engines{tuple_delimiter}7)
{record_delimiter}
("relationship"{tuple_delimiter}SCOTT, A. E.{tuple_delimiter}GENERATIVE SEARCH ENGINES{tuple_delimiter}A. E. Scott authored a paper on generative search engines{tuple_delimiter}7)
{record_delimiter}
("relationship"{tuple_delimiter}SARKAR, A.{tuple_delimiter}GENERATIVE SEARCH ENGINES{tuple_delimiter}A. Sarkar authored a paper on generative search engines{tuple_delimiter}7)
{record_delimiter}
("relationship"{tuple_delimiter}SELLEN, A.{tuple_delimiter}GENERATIVE SEARCH ENGINES{tuple_delimiter}A. Sellen authored a paper on generative search engines{tuple_delimiter}7)
{record_delimiter}
("relationship"{tuple_delimiter}RINTEL, S.{tuple_delimiter}GENERATIVE SEARCH ENGINES{tuple_delimiter}S. Rintel authored a paper on generative search engines{tuple_delimiter}7)
{record_delimiter}
("relationship"{tuple_delimiter}TARABORELLI, D.{tuple_delimiter}WIKIMEDIA{tuple_delimiter}D. Taraborelli authored a paper on Wikimedia's research agenda{tuple_delimiter}6)
{record_delimiter}
("relationship"{tuple_delimiter}ICWSM{tuple_delimiter}WIKIMEDIA{tuple_delimiter}Wikimedia's research agenda was presented at the ICWSM conference{tuple_delimiter}5)
>>>>>>> origin/main
{completion_delimiter}
#############################


Example 2:

text:
<<<<<<< HEAD
Liu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., and Liang, P. (2023). Lost
in the middle: How language models use long contexts. arXiv:2307.03172.
Liu, Y . and Lapata, M. (2019). Hierarchical transformers for multi-document summarization. arXiv
preprint arXiv:1905.13164 .
LlamaIndex (2024). LlamaIndex Knowledge Graph Index. https://docs .llamaindex .ai/en/stable/
examples/index structs/knowledge graph/KnowledgeGraphDemo .html.
Manakul, P., Liusie, A., and Gales, M. J. (2023). Selfcheckgpt: Zero-resource black-box hallucina-
tion detection for generative large language models. arXiv preprint ar
------------------------
output:
("entity"{tuple_delimiter}LIU, N. F.{tuple_delimiter}PERSON{tuple_delimiter}Liu, N. F. is an author of the paper "Lost in the middle: How language models use long contexts" published in 2023)
{record_delimiter}
("entity"{tuple_delimiter}LIN, K.{tuple_delimiter}PERSON{tuple_delimiter}Lin, K. is an author of the paper "Lost in the middle: How language models use long contexts" published in 2023)
{record_delimiter}
("entity"{tuple_delimiter}HEWITT, J.{tuple_delimiter}PERSON{tuple_delimiter}Hewitt, J. is an author of the paper "Lost in the middle: How language models use long contexts" published in 2023)
{record_delimiter}
("entity"{tuple_delimiter}PARANJAPE, A.{tuple_delimiter}PERSON{tuple_delimiter}Paranjape, A. is an author of the paper "Lost in the middle: How language models use long contexts" published in 2023)
{record_delimiter}
("entity"{tuple_delimiter}BEVILACQUA, M.{tuple_delimiter}PERSON{tuple_delimiter}Bevilacqua, M. is an author of the paper "Lost in the middle: How language models use long contexts" published in 2023)
{record_delimiter}
("entity"{tuple_delimiter}PETRONI, F.{tuple_delimiter}PERSON{tuple_delimiter}Petroni, F. is an author of the paper "Lost in the middle: How language models use long contexts" published in 2023)
{record_delimiter}
("entity"{tuple_delimiter}LIANG, P.{tuple_delimiter}PERSON{tuple_delimiter}Liang, P. is an author of the paper "Lost in the middle: How language models use long contexts" published in 2023)
{record_delimiter}
("entity"{tuple_delimiter}LIU, Y.{tuple_delimiter}PERSON{tuple_delimiter}Liu, Y. is an author of the paper "Hierarchical transformers for multi-document summarization" published in 2019)
{record_delimiter}
("entity"{tuple_delimiter}LAPATA, M.{tuple_delimiter}PERSON{tuple_delimiter}Lapata, M. is an author of the paper "Hierarchical transformers for multi-document summarization" published in 2019)
{record_delimiter}
("entity"{tuple_delimiter}LLAMAINDEX{tuple_delimiter}ORGANIZATION{tuple_delimiter}LlamaIndex is an organization that developed the LlamaIndex Knowledge Graph Index, with documentation available online)
{record_delimiter}
("entity"{tuple_delimiter}MANAKUL, P.{tuple_delimiter}PERSON{tuple_delimiter}Manakul, P. is an author of the paper "Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models" published in 2023)
{record_delimiter}
("entity"{tuple_delimiter}LIUSIE, A.{tuple_delimiter}PERSON{tuple_delimiter}Liusie, A. is an author of the paper "Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models" published in 2023)
{record_delimiter}
("entity"{tuple_delimiter}GALES, M. J.{tuple_delimiter}PERSON{tuple_delimiter}Gales, M. J. is an author of the paper "Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models" published in 2023)
{record_delimiter}
("entity"{tuple_delimiter}LOST IN THE MIDDLE: HOW LANGUAGE MODELS USE LONG CONTEXTS{tuple_delimiter}DOCUMENT{tuple_delimiter}A paper published in 2023 by Liu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., and Liang, P.)
{record_delimiter}
("entity"{tuple_delimiter}HIERARCHICAL TRANSFORMERS FOR MULTI-DOCUMENT SUMMARIZATION{tuple_delimiter}DOCUMENT{tuple_delimiter}A paper published in 2019 by Liu, Y. and Lapata, M.)
{record_delimiter}
("entity"{tuple_delimiter}SELFCHECKGPT: ZERO-RESOURCE BLACK-BOX HALLUCINATION DETECTION FOR GENERATIVE LARGE LANGUAGE MODELS{tuple_delimiter}DOCUMENT{tuple_delimiter}A paper published in 2023 by Manakul, P., Liusie, A., and Gales, M. J.)
{record_delimiter}
("relationship"{tuple_delimiter}LIU, N. F.{tuple_delimiter}LOST IN THE MIDDLE: HOW LANGUAGE MODELS USE LONG CONTEXTS{tuple_delimiter}Liu, N. F. is an author of the paper "Lost in the middle: How language models use long contexts"{tuple_delimiter}9)
{record_delimiter}
("relationship"{tuple_delimiter}LIN, K.{tuple_delimiter}LOST IN THE MIDDLE: HOW LANGUAGE MODELS USE LONG CONTEXTS{tuple_delimiter}Lin, K. is an author of the paper "Lost in the middle: How language models use long contexts"{tuple_delimiter}9)
{record_delimiter}
("relationship"{tuple_delimiter}HEWITT, J.{tuple_delimiter}LOST IN THE MIDDLE: HOW LANGUAGE MODELS USE LONG CONTEXTS{tuple_delimiter}Hewitt, J. is an author of the paper "Lost in the middle: How language models use long contexts"{tuple_delimiter}9)
{record_delimiter}
("relationship"{tuple_delimiter}PARANJAPE, A.{tuple_delimiter}LOST IN THE MIDDLE: HOW LANGUAGE MODELS USE LONG CONTEXTS{tuple_delimiter}Paranjape, A. is an author of the paper "Lost in the middle: How language models use long contexts"{tuple_delimiter}9)
{record_delimiter}
("relationship"{tuple_delimiter}BEVILACQUA, M.{tuple_delimiter}LOST IN THE MIDDLE: HOW LANGUAGE MODELS USE LONG CONTEXTS{tuple_delimiter}Bevilacqua, M. is an author of the paper "Lost in the middle: How language models use long contexts"{tuple_delimiter}9)
{record_delimiter}
("relationship"{tuple_delimiter}PETRONI, F.{tuple_delimiter}LOST IN THE MIDDLE: HOW LANGUAGE MODELS USE LONG CONTEXTS{tuple_delimiter}Petroni, F. is an author of the paper "Lost in the middle: How language models use long contexts"{tuple_delimiter}9)
{record_delimiter}
("relationship"{tuple_delimiter}LIANG, P.{tuple_delimiter}LOST IN THE MIDDLE: HOW LANGUAGE MODELS USE LONG CONTEXTS{tuple_delimiter}Liang, P. is an author of the paper "Lost in the middle: How language models use long contexts"{tuple_delimiter}9)
{record_delimiter}
("relationship"{tuple_delimiter}LIU, Y.{tuple_delimiter}HIERARCHICAL TRANSFORMERS FOR MULTI-DOCUMENT SUMMARIZATION{tuple_delimiter}Liu, Y. is an author of the paper "Hierarchical transformers for multi-document summarization"{tuple_delimiter}9)
{record_delimiter}
("relationship"{tuple_delimiter}LAPATA, M.{tuple_delimiter}HIERARCHICAL TRANSFORMERS FOR MULTI-DOCUMENT SUMMARIZATION{tuple_delimiter}Lapata, M. is an author of the paper "Hierarchical transformers for multi-document summarization"{tuple_delimiter}9)
{record_delimiter}
("relationship"{tuple_delimiter}MANAKUL, P.{tuple_delimiter}SELFCHECKGPT: ZERO-RESOURCE BLACK-BOX HALLUCINATION DETECTION FOR GENERATIVE LARGE LANGUAGE MODELS{tuple_delimiter}Manakul, P. is an author of the paper "Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models"{tuple_delimiter}9)
{record_delimiter}
("relationship"{tuple_delimiter}LIUSIE, A.{tuple_delimiter}SELFCHECKGPT: ZERO-RESOURCE BLACK-BOX HALLUCINATION DETECTION FOR GENERATIVE LARGE LANGUAGE MODELS{tuple_delimiter}Liusie, A. is an author of the paper "Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models"{tuple_delimiter}9)
{record_delimiter}
("relationship"{tuple_delimiter}GALES, M. J.{tuple_delimiter}SELFCHECKGPT: ZERO-RESOURCE BLACK-BOX HALLUCINATION DETECTION FOR GENERATIVE LARGE LANGUAGE MODELS{tuple_delimiter}Gales, M. J. is an author of the paper "Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models"{tuple_delimiter}9)
=======
: The Second Microsoft Report on AI and Productivity 
Research. Microsoft.  
1 INTRODUCTION  
There is tremendous  interest in how AI can increase people’s 
productivity at work. To help meet this interest, in December 2023, 
Microsoft released a first AI and Productivity Report  (Cambon et 
al. 2023) synthesizing the results of many Microsoft studies on AI 
and productivity. These studies contributed to a large and growing 
literature from around the world and a wide variety of disciplines. 
Although there are exceptions, this literature largely points to a 
broad conclusion: Generative AI tools have the potential to 
introduce  a substantial step -function increase in productivity for tasks performed by information workers (e.g. , Noy and Zhang 
2023; Dell’Acqua et al. 2023; Brynjolfsson et al. 2023; Peng et al. 
2023). 
 
However, much of this
------------------------
output:
("entity"{tuple_delimiter}MICROSOFT{tuple_delimiter}ORGANIZATION{tuple_delimiter}Microsoft is a technology company that released the AI and Productivity Report)
{record_delimiter}
("entity"{tuple_delimiter}AI AND PRODUCTIVITY REPORT{tuple_delimiter}DOCUMENT{tuple_delimiter}The AI and Productivity Report is a document released by Microsoft in December 2023, synthesizing results of studies on AI and productivity)
{record_delimiter}
("entity"{tuple_delimiter}CAMBON ET AL. 2023{tuple_delimiter}STUDY{tuple_delimiter}A study included in the AI and Productivity Report, authored by Cambon and others in 2023)
{record_delimiter}
("entity"{tuple_delimiter}NOY AND ZHANG 2023{tuple_delimiter}STUDY{tuple_delimiter}A study referenced in the AI and Productivity Report, authored by Noy and Zhang in 2023)
{record_delimiter}
("entity"{tuple_delimiter}DELL’ACQUA ET AL. 2023{tuple_delimiter}STUDY{tuple_delimiter}A study referenced in the AI and Productivity Report, authored by Dell’Acqua and others in 2023)
{record_delimiter}
("entity"{tuple_delimiter}BRYNJOLFSSON ET AL. 2023{tuple_delimiter}STUDY{tuple_delimiter}A study referenced in the AI and Productivity Report, authored by Brynjolfsson and others in 2023)
{record_delimiter}
("entity"{tuple_delimiter}PENG ET AL. 2023{tuple_delimiter}STUDY{tuple_delimiter}A study referenced in the AI and Productivity Report, authored by Peng and others in 2023)
{record_delimiter}
("entity"{tuple_delimiter}GENERATIVE AI TOOLS{tuple_delimiter}TECHNOLOGY{tuple_delimiter}Generative AI tools are technologies that have the potential to significantly increase productivity for information workers)
{record_delimiter}
("relationship"{tuple_delimiter}MICROSOFT{tuple_delimiter}AI AND PRODUCTIVITY REPORT{tuple_delimiter}Microsoft released the AI and Productivity Report in December 2023{tuple_delimiter}9)
{record_delimiter}
("relationship"{tuple_delimiter}AI AND PRODUCTIVITY REPORT{tuple_delimiter}CAMBON ET AL. 2023{tuple_delimiter}The AI and Productivity Report includes the study by Cambon et al. 2023{tuple_delimiter}8)
{record_delimiter}
("relationship"{tuple_delimiter}AI AND PRODUCTIVITY REPORT{tuple_delimiter}NOY AND ZHANG 2023{tuple_delimiter}The AI and Productivity Report references the study by Noy and Zhang 2023{tuple_delimiter}8)
{record_delimiter}
("relationship"{tuple_delimiter}AI AND PRODUCTIVITY REPORT{tuple_delimiter}DELL’ACQUA ET AL. 2023{tuple_delimiter}The AI and Productivity Report references the study by Dell’Acqua et al. 2023{tuple_delimiter}8)
{record_delimiter}
("relationship"{tuple_delimiter}AI AND PRODUCTIVITY REPORT{tuple_delimiter}BRYNJOLFSSON ET AL. 2023{tuple_delimiter}The AI and Productivity Report references the study by Brynjolfsson et al. 2023{tuple_delimiter}8)
{record_delimiter}
("relationship"{tuple_delimiter}AI AND PRODUCTIVITY REPORT{tuple_delimiter}PENG ET AL. 2023{tuple_delimiter}The AI and Productivity Report references the study by Peng et al. 2023{tuple_delimiter}8)
{record_delimiter}
("relationship"{tuple_delimiter}GENERATIVE AI TOOLS{tuple_delimiter}INFORMATION WORKERS{tuple_delimiter}Generative AI tools have the potential to significantly increase productivity for information workers{tuple_delimiter}7)
>>>>>>> origin/main
{completion_delimiter}
#############################



-Real Data-
######################
text: {input_text}
######################
output:
