Triple

T2364433
Position Surface form Disambiguated ID Type / Status
Subject Riemann integral E47347 entity
Predicate approximatedBy P4447 FINISHED
Object Simpson's rule
Simpson's rule is a numerical integration technique that approximates the area under a curve by fitting parabolas through groups of data points.
E259761 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: Simpson's rule | Statement: [Riemann integral, approximatedBy, Simpson's rule]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Simpson's rule
Context triple: [Riemann integral, approximatedBy, Simpson's rule]
  • A. Riemann sums
    Riemann sums are a fundamental method in calculus for approximating the area under a curve by summing the areas of a sequence of rectangles, forming the basis of the definition of the definite integral.
  • B. Euler’s method for numerical integration
    Euler’s method for numerical integration is a simple first-order numerical procedure used to approximate solutions to ordinary differential equations by stepping forward in small increments.
  • C. Euler–Maclaurin summation formula
    The Euler–Maclaurin summation formula is a fundamental result in analysis that connects sums and integrals, providing powerful asymptotic expansions and error estimates for approximating series by integrals.
  • D. Milstein method
    The Milstein method is a numerical scheme for solving stochastic differential equations that improves on the Euler–Maruyama method by including derivative terms of the diffusion coefficient for higher accuracy.
  • E. Halley’s method for solving equations
    Halley’s method for solving equations is an iterative numerical algorithm, related to and faster-converging than Newton’s method, used to find approximate roots of equations.
  • 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: Simpson's rule
Triple: [Riemann integral, approximatedBy, Simpson's rule]
Generated description
Simpson's rule is a numerical integration technique that approximates the area under a curve by fitting parabolas through groups of data points.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Simpson's rule
Target entity description: Simpson's rule is a numerical integration technique that approximates the area under a curve by fitting parabolas through groups of data points.
  • A. Riemann sums
    Riemann sums are a fundamental method in calculus for approximating the area under a curve by summing the areas of a sequence of rectangles, forming the basis of the definition of the definite integral.
  • B. Euler’s method for numerical integration
    Euler’s method for numerical integration is a simple first-order numerical procedure used to approximate solutions to ordinary differential equations by stepping forward in small increments.
  • C. Euler–Maclaurin summation formula
    The Euler–Maclaurin summation formula is a fundamental result in analysis that connects sums and integrals, providing powerful asymptotic expansions and error estimates for approximating series by integrals.
  • D. Milstein method
    The Milstein method is a numerical scheme for solving stochastic differential equations that improves on the Euler–Maruyama method by including derivative terms of the diffusion coefficient for higher accuracy.
  • E. Halley’s method for solving equations
    Halley’s method for solving equations is an iterative numerical algorithm, related to and faster-converging than Newton’s method, used to find approximate roots of equations.
  • 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_69a88a1a4a6081908645b0f2914521ab completed March 4, 2026, 7:38 p.m.
NER Named-entity recognition batch_69abd0d813dc8190aa331cdca0b75eca completed March 7, 2026, 7:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69aea896e0388190aabff2d70787dc43 completed March 9, 2026, 11:01 a.m.
NEDg Description generation batch_69aea91ce164819091aa24b287f9fb8e completed March 9, 2026, 11:03 a.m.
NED2 Entity disambiguation (via description) batch_69aea999b864819084134c670e7c5d9c completed March 9, 2026, 11:06 a.m.
Created at: March 4, 2026, 7:55 p.m.