Triple

T14911038
Position Surface form Disambiguated ID Type / Status
Subject Tukey's biweight E371259 entity
Predicate influencedBy P9 FINISHED
Object M-estimation theory
M-estimation theory is a general statistical framework for parameter estimation that extends maximum likelihood methods to robustly handle outliers and model deviations by minimizing suitably chosen objective functions.
E1127494 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: M-estimation theory | Statement: [Tukey's biweight, influencedBy, M-estimation theory]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: M-estimation theory
Context triple: [Tukey's biweight, influencedBy, M-estimation theory]
  • A. Generalized method of moments
    The generalized method of moments is an econometric estimation technique that uses sample moments to infer model parameters without requiring full specification of the underlying probability distribution.
  • B. Mathematical Methods of Statistics
    Mathematical Methods of Statistics is a foundational 1946 textbook that helped formalize modern mathematical statistics, particularly in the areas of probability theory and statistical inference.
  • C. Neyman–Pearson theory of hypothesis testing
    The Neyman–Pearson theory of hypothesis testing is a foundational statistical framework that formalizes how to construct and evaluate tests for competing hypotheses using concepts like Type I and Type II errors and power.
  • D. Wald estimator
    The Wald estimator is a statistical method used in econometrics and causal inference to estimate parameters by dividing an estimated effect by its standard error, forming the basis of the Wald test.
  • E. Statistical Decision Functions
    Statistical Decision Functions is a foundational work in decision theory and statistics that systematically develops the theory of optimal decision-making under uncertainty.
  • 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: M-estimation theory
Triple: [Tukey's biweight, influencedBy, M-estimation theory]
Generated description
M-estimation theory is a general statistical framework for parameter estimation that extends maximum likelihood methods to robustly handle outliers and model deviations by minimizing suitably chosen objective functions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: M-estimation theory
Target entity description: M-estimation theory is a general statistical framework for parameter estimation that extends maximum likelihood methods to robustly handle outliers and model deviations by minimizing suitably chosen objective functions.
  • A. Generalized method of moments
    The generalized method of moments is an econometric estimation technique that uses sample moments to infer model parameters without requiring full specification of the underlying probability distribution.
  • B. Mathematical Methods of Statistics
    Mathematical Methods of Statistics is a foundational 1946 textbook that helped formalize modern mathematical statistics, particularly in the areas of probability theory and statistical inference.
  • C. Neyman–Pearson theory of hypothesis testing
    The Neyman–Pearson theory of hypothesis testing is a foundational statistical framework that formalizes how to construct and evaluate tests for competing hypotheses using concepts like Type I and Type II errors and power.
  • D. Wald estimator
    The Wald estimator is a statistical method used in econometrics and causal inference to estimate parameters by dividing an estimated effect by its standard error, forming the basis of the Wald test.
  • E. Statistical Decision Functions
    Statistical Decision Functions is a foundational work in decision theory and statistics that systematically develops the theory of optimal decision-making under uncertainty.
  • 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_69d85cc7ea3481908228b5acb7d06f12 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded61c6b9c8190a92934d49b98fe46 completed April 15, 2026, 12:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe72bb366481909706d511f5ae1290 completed May 8, 2026, 11:33 p.m.
NEDg Description generation batch_69fe733a580c8190bc2f053188bb7145 completed May 8, 2026, 11:35 p.m.
NED2 Entity disambiguation (via description) batch_69fe75ecce8c8190a879d8f908d9fb28 completed May 8, 2026, 11:46 p.m.
Created at: April 10, 2026, 2:26 a.m.