Triple

T22147297
Position Surface form Disambiguated ID Type / Status
Subject Second Five-Year Plan of India E547319 entity
Predicate economicModel P252 FINISHED
Object Mahalanobis model NE NERFINISHED

How this triple was built (3 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: Mahalanobis model | Statement: [Second Five-Year Plan of India, economicModel, Mahalanobis model]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mahalanobis model
Context triple: [Second Five-Year Plan of India, economicModel, Mahalanobis model]
  • A. Mahalanobis distance
    Mahalanobis distance is a multivariate measure of the distance between a point and a distribution (or between distributions) that accounts for correlations between variables via the covariance matrix.
  • B. Hotelling’s T-squared distribution
    Hotelling’s T-squared distribution is a multivariate generalization of Student’s t-distribution used primarily for hypothesis testing and constructing confidence regions for mean vectors in multivariate statistics.
  • C. Gaussian mixture models
    Gaussian mixture models are probabilistic clustering models that represent data as a combination of multiple Gaussian distributions, allowing soft cluster assignments and more flexible cluster shapes than KMeans.
  • D. Bayesian model averaging
    Bayesian model averaging is a statistical technique that combines predictions from multiple models by weighting them according to their posterior probabilities to account for model uncertainty.
  • E. Tobit model
    The Tobit model is an econometric regression model designed for situations where the dependent variable is censored, allowing consistent estimation when observations are only partially observed beyond certain limits.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Mahalanobis model
Target entity description: The Mahalanobis model is an economic planning framework that emphasizes rapid industrialization through heavy industry and capital goods, heavily influencing India’s early Five-Year Plans.
  • A. Mahalanobis distance
    Mahalanobis distance is a multivariate measure of the distance between a point and a distribution (or between distributions) that accounts for correlations between variables via the covariance matrix.
  • B. Hotelling’s T-squared distribution
    Hotelling’s T-squared distribution is a multivariate generalization of Student’s t-distribution used primarily for hypothesis testing and constructing confidence regions for mean vectors in multivariate statistics.
  • C. Gaussian mixture models
    Gaussian mixture models are probabilistic clustering models that represent data as a combination of multiple Gaussian distributions, allowing soft cluster assignments and more flexible cluster shapes than KMeans.
  • D. Bayesian model averaging
    Bayesian model averaging is a statistical technique that combines predictions from multiple models by weighting them according to their posterior probabilities to account for model uncertainty.
  • E. Tobit model
    The Tobit model is an econometric regression model designed for situations where the dependent variable is censored, allowing consistent estimation when observations are only partially observed beyond certain limits.
  • F. None of above. chosen

Provenance (2 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_69e11e3b52088190ad5df386d01eb2fb completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f129f156988190bc9a24a37418e849 completed April 28, 2026, 9:43 p.m.
Created at: April 16, 2026, 8:33 p.m.