relation/holdout.jsonl

{"input": "Source: Deloitte own elaboration based on SEIS (May-June 2024) UNHCR (2024) survey.", "output": {"entities": {"named_data": ["SEIS"], "organization": ["UNHCR", "Deloitte"], "year": ["2024"]}, "relations": [{"has_organization": {"head": "SEIS", "tail": "UNHCR"}}, {"used_by": {"head": "SEIS", "tail": "Deloitte"}}, {"has_timeframe": {"head": "SEIS", "tail": "2024"}}]}, "_meta": {"source": "real_world_verified_pdf"}}
{"input": "Source: Deloitte own elaboration based on Polish Border Guard Headquarters and PESEL data.", "output": {"entities": {"named_data": ["PESEL data"], "organization": ["Deloitte", "Polish Border Guard Headquarters"]}, "relations": [{"has_organization": {"head": "PESEL data", "tail": "Polish Border Guard Headquarters"}}, {"used_by": {"head": "PESEL data", "tail": "Deloitte"}}]}, "_meta": {"source": "real_world_verified_pdf"}}
{"input": "Source: Deloitte own elaboration based on ZUS data and GUS population data as of mid-2024.", "output": {"entities": {"named_data": ["ZUS data", "GUS population data"], "organization": ["Deloitte"], "year": ["2024"]}, "relations": [{"used_by": {"head": "ZUS data", "tail": "Deloitte"}}, {"used_by": {"head": "GUS population data", "tail": "Deloitte"}}, {"has_timeframe": {"head": "GUS population data", "tail": "2024"}}]}, "_meta": {"source": "real_world_verified_pdf"}}
{"input": "Multi-Sector Needs Assessment (MSNA) survey, conducted in July and August 2024, provides data on refugee vulnerabilities in Poland.", "output": {"entities": {"named_data": ["Multi-Sector Needs Assessment"], "acronym": ["MSNA"], "year": ["2024"], "geography": ["Poland"]}, "relations": [{"has_acronym": {"head": "Multi-Sector Needs Assessment", "tail": "MSNA"}}, {"has_timeframe": {"head": "Multi-Sector Needs Assessment", "tail": "2024"}}, {"has_geography": {"head": "Multi-Sector Needs Assessment", "tail": "Poland"}}]}, "_meta": {"source": "real_world_verified_pdf"}}
{"input": "UNHCR data portal provides refugee statistics. The Labour Force Survey (LFS) from Eurostat is used for employment data.", "output": {"entities": {"named_data": ["UNHCR data portal", "Labour Force Survey"], "organization": ["UNHCR", "Eurostat"], "acronym": ["LFS"]}, "relations": [{"has_organization": {"head": "UNHCR data portal", "tail": "UNHCR"}}, {"has_organization": {"head": "Labour Force Survey", "tail": "Eurostat"}}, {"has_acronym": {"head": "Labour Force Survey", "tail": "LFS"}}]}, "_meta": {"source": "real_world_verified_pdf"}}
{"input": "The data for this paper come from the European Social Survey (ESS) for the survey years 2002,\n\nobservations per country/year. The ESS covers 36 countries, 24 of which are included in our\n\nCard et al (2012) investigates the drivers of attitudes towards immigrants in Europe using,\n\n\namong others, the following variables of the ESS:", "output": {"entities": {"named_data": ["European Social Survey"], "acronym": ["ESS"]}, "relations": [{"has_acronym": {"head": "European Social Survey", "tail": "ESS"}}]}, "_meta": {"source": "real_world_verified"}}
{"input": "(henceforth CRU), provided by the Climatic Research Unit of the University of East\n\nand satellite-based observations. The data enable us to characterize historical climate\n\n<!-- PAGE 6 -->\n**2.1 Assignment of reliability weights to the eight GCMs, based on their**\n**historical \"goodness of fit\" to the CRU data**", "output": {"entities": {"named_data": ["CRU"], "organization": ["Climatic Research Unit"]}, "relations": [{"has_organization": {"head": "CRU", "tail": "Climatic Research Unit"}}]}, "_meta": {"source": "real_world_verified"}}
{"input": "The firm-level financial data for 1997 are primarily from the Worldscope database. The World\n\nin Australia and Canada, respectively. Using industry data from Mexico, Blomstrom and Persson\n\nCommission on an annual basis. We use [group-affiliation data from the 1994-1997 lists of business]\n\nby the level of growth of a sector or a country. Indeed, using firm level data, Haddad and Harrison", "output": {"entities": {"named_data": ["Worldscope database"]}, "relations": []}, "_meta": {"source": "real_world_verified"}}
{"input": "Figure 4: Refugees and Asylum-Seekers by Migratory Path 1951 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database", "UNHCR Global Trends"], "organization": ["UNHCR"], "year": ["2015"]}, "relations": [{"has_organization": {"head": "UNHCR Statistical Online Population Database", "tail": "UNHCR"}}, {"has_organization": {"head": "UNHCR Global Trends", "tail": "UNHCR"}}, {"has_timeframe": {"head": "UNHCR Global Trends", "tail": "2015"}}]}, "_meta": {"source": "real_world_verified"}}
{"input": "<!-- PAGE 9 -->\n|Figure 6a Share of population working primarily<br>in agriculture (%) in mining communes|Figure 6b Share of population working primarily<br>in extractives (%) in mining communes|\n|---|---|\n|||\n|_Source:_ RGPH (General Population and Housing Census) 1998 and 2009.|_Source:_ RGPH (General Population and Housing Census) 1998 and 2009.|", "output": {"entities": {"named_data": ["RGPH"], "organization": ["General Population and Housing Census"], "year": ["1998", "2009"]}, "relations": [{"has_organization": {"head": "RGPH", "tail": "General Population and Housing Census"}}, {"has_timeframe": {"head": "RGPH", "tail": "1998"}}, {"has_timeframe": {"head": "RGPH", "tail": "2009"}}]}, "_meta": {"source": "real_world_verified"}}
{"input": "National Poverty line 32% 84% Food security High food insecurity 26% 67% Food insecurity scale 4.0 8.1 Social cohesion Economic competition 33% 49% Increased insecurity 37% 39% Source: Pape et al. (2018) and World Bank Staff based on SESRE 2023.", "output": {"entities": {"named_data": ["SESRE"], "year": ["2023"], "organization": ["World Bank"]}, "relations": [{"has_timeframe": {"head": "SESRE", "tail": "2023"}}, {"has_organization": {"head": "SESRE", "tail": "World Bank"}}]}, "_meta": {"source": "real_world_verified"}}
{"input": "We use two main data sources for our estimation of a poverty line for Brazil: the 2017/18 Household Budget Survey (Pesquisa de Orçamentos Familares; POF) and the Brazilian Table of Food Composition (Tabela Brasileira de Composição de Alimentos; TBCA). POF is a nationally representative semiregular survey on income and expenditures in Brazil, conducted every six to nine years.", "output": {"entities": {"named_data": ["Household Budget Survey", "Brazilian Table of Food Composition"], "acronym": ["POF", "TBCA"], "year": ["2017/18"], "geography": ["Brazil"]}, "relations": [{"has_acronym": {"head": "Household Budget Survey", "tail": "POF"}}, {"has_acronym": {"head": "Brazilian Table of Food Composition", "tail": "TBCA"}}, {"has_timeframe": {"head": "Household Budget Survey", "tail": "2017/18"}}, {"has_geography": {"head": "Household Budget Survey", "tail": "Brazil"}}, {"has_geography": {"head": "Brazilian Table of Food Composition", "tail": "Brazil"}}]}, "_meta": {"source": "real_world_verified"}}
{"input": "effects is Calahorrano (2011). Using panel data for Germany between 1999 and 2008, she finds   that immigration concerns decrease over the life-cycle.\n\nsimilar to Calahorrano (2011). However, given the lack of comparable panel data surveys for a   large group of countries, we use pooled cross-sections from the European Social Survey (ESS)", "output": {"entities": {"named_data": ["British Household Panel Survey"]}, "relations": []}, "_meta": {"source": "real_world_verified"}}
{"input": "To mitigate issues arising from the time interval between the 2007 Population Census and EMDHS 2014, only\n\nEMDHS at regional level. The table shows that measured undernutrition rates in EMDHS and the estimated rates\n\nregression of z-scores is estimated in the EMDHS with addition of the SAE estimates. The regression includes the", "output": {"entities": {"named_data": ["Population Census", "EMDHS"], "year": ["2007", "2014"]}, "relations": [{"has_timeframe": {"head": "Population Census", "tail": "2007"}}, {"has_timeframe": {"head": "EMDHS", "tail": "2014"}}]}, "_meta": {"source": "real_world_verified"}}
{"input": "6 However, this may reflect differences in rainfall or farming intensity.\n7 We use two poverty maps based on the General Population and Housing Censuses of 1998 and 2009. The poverty\nmap for 1998 was developed by GREAT (Applied and Theoretical Economics Research Group) and combines the\n1998 census and the household survey ELIM (Integrated Light Household Survey) of 2006.", "output": {"entities": {"named_data": ["General Population and Housing Censuses", "ELIM"], "year": ["1998", "2009", "2006"]}, "relations": [{"has_timeframe": {"head": "General Population and Housing Censuses", "tail": "1998"}}, {"has_timeframe": {"head": "General Population and Housing Censuses", "tail": "2009"}}, {"has_timeframe": {"head": "ELIM", "tail": "2006"}}]}, "_meta": {"source": "real_world_verified"}}
{"input": "Figure 4: Refugees and Asylum-Seekers by Migratory Path 1951 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database", "UNHCR Global Trends"], "organization": ["UNHCR"], "year": ["2015"]}, "relations": [{"has_organization": {"head": "UNHCR Statistical Online Population Database", "tail": "UNHCR"}}, {"has_organization": {"head": "UNHCR Global Trends", "tail": "UNHCR"}}, {"has_timeframe": {"head": "UNHCR Global Trends", "tail": "2015"}}]}, "_meta": {"source": "real_world_verified"}}
{"input": "_Source: Authors' calculation based on Dantlait survey data._\n\n_Source: Dantlait survey_\n\nin 2009 and 2010 for the Livestock Climate and Society (ECliS) project (final report and", "output": {"entities": {"named_data": ["Dantlait survey data"], "year": ["2009", "2010"]}, "relations": [{"has_timeframe": {"head": "Dantlait survey data", "tail": "2009"}}, {"has_timeframe": {"head": "Dantlait survey data", "tail": "2010"}}]}, "_meta": {"source": "real_world_verified"}}
{"input": "Figure 4: Refugees and Asylum-Seekers by Migratory Path 1951 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database", "UNHCR Global Trends"], "organization": ["UNHCR"], "year": ["2015"]}, "relations": [{"has_organization": {"head": "UNHCR Statistical Online Population Database", "tail": "UNHCR"}}, {"has_organization": {"head": "UNHCR Global Trends", "tail": "UNHCR"}}, {"has_timeframe": {"head": "UNHCR Global Trends", "tail": "2015"}}]}, "_meta": {"source": "real_world_verified"}}
{"input": "evidence is consistent with studies that used cross-section surveys, panel data and cross-country\n\nSome of the work with panel data has also gone further in an effort to establish a causality link\n\nOswald (2007) use information on lottery winnings in the British Household Panel Survey", "output": {"entities": {"named_data": ["British Household Panel Survey"]}, "relations": []}, "_meta": {"source": "real_world_verified"}}
{"input": "3 As of December 2023, according to UNHCR, based on governmental sources [Situation Ukraine Refugee Situation (unhcr.org)](https://data.unhcr.org/en/situations/ukraine) 4 According to the active PESEL UKR database.\n5 According to the active PESEL UKR database in October 2023.\n6 Deloitte calculations based on Multi-Sector Needs Assessment Poland 2023 survey data provided by UNHCR.\n\n06", "output": {"entities": {"named_data": ["PESEL UKR database", "Multi-Sector Needs Assessment Poland 2023"], "year": ["2023"], "geography": ["Poland"], "organization": ["UNHCR"]}, "relations": [{"has_timeframe": {"head": "PESEL UKR database", "tail": "2023"}}, {"has_geography": {"head": "PESEL UKR database", "tail": "Poland"}}, {"has_timeframe": {"head": "Multi-Sector Needs Assessment Poland 2023", "tail": "2023"}}, {"has_organization": {"head": "Multi-Sector Needs Assessment Poland 2023", "tail": "UNHCR"}}, {"has_geography": {"head": "Multi-Sector Needs Assessment Poland 2023", "tail": "Poland"}}]}, "_meta": {"source": "real_world_verified"}}
{"input": "1000 500 0 65+ 55-64 45-54 35-44 25-34 18-24 <18 25% 20% 15% 10% 5% 0% 5% 10% 15% 20% 25%\n\n**Source:** Deloitte own elaboration based on the PESEL database as of October 2023\n\n**Chart 5.** Composition of refugee households in Poland\n\n60% 50% Total entries-exits of the Polish-Ukrainian border Pesel data\n\n**Source:** Deloitte own elaboration based on Polish Border Guard Headquarter and PESEL data.", "output": {"entities": {"named_data": ["PESEL database"], "year": ["2023"], "geography": ["Poland"]}, "relations": [{"has_timeframe": {"head": "PESEL database", "tail": "2023"}}, {"has_geography": {"head": "PESEL database", "tail": "Poland"}}]}, "_meta": {"source": "real_world_verified"}}