Triple

T9747018
Position Surface form Disambiguated ID Type / Status
Subject Alberto Calderón E236336 entity
Predicate notableFor P22 FINISHED
Object Calderón problem in inverse conductivity
The Calderón problem in inverse conductivity is a fundamental question in mathematical inverse problems that asks whether one can determine the electrical conductivity inside a medium uniquely from voltage and current measurements made at its boundary.
E817947 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: Calderón problem in inverse conductivity | Statement: [Alberto Calderón, notableFor, Calderón problem in inverse conductivity]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Calderón problem in inverse conductivity
Context triple: [Alberto Calderón, notableFor, Calderón problem in inverse conductivity]
  • A. Hadamard’s example of ill-posed problems
    Hadamard’s example of ill-posed problems is a classical mathematical construction illustrating how small changes in input data can cause large, unstable changes in solutions, thereby violating the standard criteria for well-posedness in analysis and partial differential equations.
  • B. Agmon–Douglis–Nirenberg estimates
    Agmon–Douglis–Nirenberg estimates are fundamental a priori estimates in the theory of linear elliptic partial differential equations and systems, providing precise control of solution regularity in terms of data norms.
  • C. Neumann boundary conditions in potential theory
    Neumann boundary conditions in potential theory specify that the normal derivative of a potential function on a boundary is prescribed, modeling situations where flux across the boundary is controlled rather than the potential itself.
  • D. Israel–Carter–Robinson uniqueness theorems
    The Israel–Carter–Robinson uniqueness theorems are a set of results in general relativity showing that stationary, asymptotically flat black holes in four-dimensional spacetime are completely characterized by just their mass, charge, and angular momentum.
  • E. Wiener–Hopf equations
    Wiener–Hopf equations are integral equations that arise in problems of filtering, prediction, and diffraction, forming the mathematical foundation for optimal linear filters such as the Wiener filter.
  • 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: Calderón problem in inverse conductivity
Triple: [Alberto Calderón, notableFor, Calderón problem in inverse conductivity]
Generated description
The Calderón problem in inverse conductivity is a fundamental question in mathematical inverse problems that asks whether one can determine the electrical conductivity inside a medium uniquely from voltage and current measurements made at its boundary.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Calderón problem in inverse conductivity
Target entity description: The Calderón problem in inverse conductivity is a fundamental question in mathematical inverse problems that asks whether one can determine the electrical conductivity inside a medium uniquely from voltage and current measurements made at its boundary.
  • A. Hadamard’s example of ill-posed problems
    Hadamard’s example of ill-posed problems is a classical mathematical construction illustrating how small changes in input data can cause large, unstable changes in solutions, thereby violating the standard criteria for well-posedness in analysis and partial differential equations.
  • B. Agmon–Douglis–Nirenberg estimates
    Agmon–Douglis–Nirenberg estimates are fundamental a priori estimates in the theory of linear elliptic partial differential equations and systems, providing precise control of solution regularity in terms of data norms.
  • C. Neumann boundary conditions in potential theory
    Neumann boundary conditions in potential theory specify that the normal derivative of a potential function on a boundary is prescribed, modeling situations where flux across the boundary is controlled rather than the potential itself.
  • D. Israel–Carter–Robinson uniqueness theorems
    The Israel–Carter–Robinson uniqueness theorems are a set of results in general relativity showing that stationary, asymptotically flat black holes in four-dimensional spacetime are completely characterized by just their mass, charge, and angular momentum.
  • E. Wiener–Hopf equations
    Wiener–Hopf equations are integral equations that arise in problems of filtering, prediction, and diffraction, forming the mathematical foundation for optimal linear filters such as the Wiener filter.
  • 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_69ca84d3e24481908a476e2231123cf9 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9f677830819096d388b9c798ecd5 completed April 1, 2026, 10:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1b00d76488190af68cba694dc329c completed April 5, 2026, 12:42 a.m.
NEDg Description generation batch_69d1b0f7a68481909de080dd78f883a8 completed April 5, 2026, 12:46 a.m.
NED2 Entity disambiguation (via description) batch_69d1b17bd97c8190b77815db5a6a70d9 completed April 5, 2026, 12:49 a.m.
Created at: March 30, 2026, 8:23 p.m.