Triple

T15878979
Position Surface form Disambiguated ID Type / Status
Subject Donald B. Rubin E385023 entity
Predicate notableConcept P201 FINISHED
Object multiple imputation for nonresponse in surveys
Multiple imputation for nonresponse in surveys is a statistical methodology that replaces missing survey data with multiple sets of plausible values to allow valid inference that accounts for uncertainty due to nonresponse.
E1181898 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: multiple imputation for nonresponse in surveys | Statement: [Donald B. Rubin, notableConcept, multiple imputation for nonresponse in surveys]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: multiple imputation for nonresponse in surveys
Context triple: [Donald B. Rubin, notableConcept, multiple imputation for nonresponse in surveys]
  • A. Statistics Surveys
    Statistics Surveys is an open-access, peer-reviewed journal that publishes survey and review articles covering a broad range of topics in statistics and probability.
  • B. A Solution to the Ecological Inference Problem
    A Solution to the Ecological Inference Problem is a influential methodological book by political scientist Gary King that introduces statistical techniques for inferring individual-level behavior from aggregate data.
  • C. Fundamental Principles of Official Statistics
    The Fundamental Principles of Official Statistics are a set of internationally agreed guidelines that define the professional and ethical standards for producing reliable, impartial, and high-quality official statistics.
  • D. “Statistical Confluence Analysis by Means of Complete Regression Systems”
    “Statistical Confluence Analysis by Means of Complete Regression Systems” is a foundational econometric work by Ragnar Frisch that develops a systematic regression-based framework for analyzing interdependent economic relationships.
  • E. “Sample Selection Bias as a Specification Error”
    “Sample Selection Bias as a Specification Error” is a landmark econometrics paper by James Heckman that introduced the Heckman correction for dealing with non-randomly selected samples in statistical analysis.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: multiple imputation for nonresponse in surveys
Triple: [Donald B. Rubin, notableConcept, multiple imputation for nonresponse in surveys]
Generated description
Multiple imputation for nonresponse in surveys is a statistical methodology that replaces missing survey data with multiple sets of plausible values to allow valid inference that accounts for uncertainty due to nonresponse.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: multiple imputation for nonresponse in surveys
Target entity description: Multiple imputation for nonresponse in surveys is a statistical methodology that replaces missing survey data with multiple sets of plausible values to allow valid inference that accounts for uncertainty due to nonresponse.
  • A. Statistics Surveys
    Statistics Surveys is an open-access, peer-reviewed journal that publishes survey and review articles covering a broad range of topics in statistics and probability.
  • B. A Solution to the Ecological Inference Problem
    A Solution to the Ecological Inference Problem is a influential methodological book by political scientist Gary King that introduces statistical techniques for inferring individual-level behavior from aggregate data.
  • C. Fundamental Principles of Official Statistics
    The Fundamental Principles of Official Statistics are a set of internationally agreed guidelines that define the professional and ethical standards for producing reliable, impartial, and high-quality official statistics.
  • D. “Statistical Confluence Analysis by Means of Complete Regression Systems”
    “Statistical Confluence Analysis by Means of Complete Regression Systems” is a foundational econometric work by Ragnar Frisch that develops a systematic regression-based framework for analyzing interdependent economic relationships.
  • E. “Sample Selection Bias as a Specification Error”
    “Sample Selection Bias as a Specification Error” is a landmark econometrics paper by James Heckman that introduced the Heckman correction for dealing with non-randomly selected samples in statistical analysis.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d86da4e86481909f1325fdc971b5ec completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e155ff96588190b8fca1c3bf4a39a2 completed April 16, 2026, 9:34 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffa9529ac48190993d1af234faea3b completed May 9, 2026, 9:38 p.m.
NEDg Description generation batch_69ffa9e9b17c8190b98d930fd5cb0723 completed May 9, 2026, 9:40 p.m.
NED2 Entity disambiguation (via description) batch_69ffaa973274819080889e1b9883b8dc completed May 9, 2026, 9:43 p.m.
Created at: April 10, 2026, 4:51 a.m.