Triple
T26867
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Richard Feynman |
E538
|
entity |
| Predicate | knownFor |
P22
|
FINISHED |
| Object |
Feynman–Hellmann theorem
The Feynman–Hellmann theorem is a result in quantum mechanics that relates the derivative of an energy eigenvalue with respect to a parameter in the Hamiltonian to the expectation value of the corresponding derivative of the Hamiltonian.
|
E1162
|
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: Feynman–Hellmann theorem | Statement: [Richard Feynman, knownFor, Feynman–Hellmann theorem]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Feynman–Hellmann theorem Context triple: [Richard Feynman, knownFor, Feynman–Hellmann theorem]
-
A.
Oppenheimer–Phillips process
The Oppenheimer–Phillips process is a nuclear reaction mechanism in which a deuteron interacting with a target nucleus effectively transfers its neutron while the proton is repelled, enabling certain reactions to occur at lower energies than would otherwise be required.
-
B.
Richard Feynman
Richard Feynman was a pioneering American theoretical physicist renowned for his work in quantum electrodynamics, his influential teaching, and his popular science writings.
-
C.
Differential analyzer
The Differential Analyzer is an early analog mechanical computer designed to solve differential equations using interconnected rotating shafts and wheels.
-
D.
Nash embedding theorem
The Nash embedding theorem is a fundamental result in differential geometry that shows any Riemannian manifold can be isometrically embedded into some Euclidean space, thereby realizing abstract curved spaces as concrete subsets of standard Euclidean space.
-
E.
Theodor
Theodor "Ted" Nelson is an American pioneer of information technology best known for coining the term "hypertext" and envisioning global hyperlinked document systems.
- 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: Feynman–Hellmann theorem Triple: [Richard Feynman, knownFor, Feynman–Hellmann theorem]
Generated description
The Feynman–Hellmann theorem is a result in quantum mechanics that relates the derivative of an energy eigenvalue with respect to a parameter in the Hamiltonian to the expectation value of the corresponding derivative of the Hamiltonian.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Feynman–Hellmann theorem Target entity description: The Feynman–Hellmann theorem is a result in quantum mechanics that relates the derivative of an energy eigenvalue with respect to a parameter in the Hamiltonian to the expectation value of the corresponding derivative of the Hamiltonian.
-
A.
Oppenheimer–Phillips process
The Oppenheimer–Phillips process is a nuclear reaction mechanism in which a deuteron interacting with a target nucleus effectively transfers its neutron while the proton is repelled, enabling certain reactions to occur at lower energies than would otherwise be required.
-
B.
Richard Feynman
Richard Feynman was a pioneering American theoretical physicist renowned for his work in quantum electrodynamics, his influential teaching, and his popular science writings.
-
C.
Differential analyzer
The Differential Analyzer is an early analog mechanical computer designed to solve differential equations using interconnected rotating shafts and wheels.
-
D.
Nash embedding theorem
The Nash embedding theorem is a fundamental result in differential geometry that shows any Riemannian manifold can be isometrically embedded into some Euclidean space, thereby realizing abstract curved spaces as concrete subsets of standard Euclidean space.
-
E.
Theodor
Theodor "Ted" Nelson is an American pioneer of information technology best known for coining the term "hypertext" and envisioning global hyperlinked document systems.
- 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_69a243b4ac2c8190b93c303df797b7b2 |
completed | Feb. 28, 2026, 1:24 a.m. |
| NER | Named-entity recognition | batch_69a246776cf48190aca9855cb07e8d89 |
completed | Feb. 28, 2026, 1:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a248e9961c8190a65bacb26fbc16e7 |
completed | Feb. 28, 2026, 1:46 a.m. |
| NEDg | Description generation | batch_69a2494198908190a6dd6f5ea5404f3f |
completed | Feb. 28, 2026, 1:47 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a249f826108190afb1dc9a7017d1b6 |
completed | Feb. 28, 2026, 1:50 a.m. |
Created at: Feb. 28, 2026, 1:34 a.m.