Triple
T17105395
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kramers turnover theory |
E415084
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object |
Pollak–Grabert–Hänggi turnover formula
The Pollak–Grabert–Hänggi turnover formula is a refined theoretical expression in chemical physics that quantitatively describes reaction rate behavior across the full range from low to high friction, extending and completing Kramers’ original turnover theory.
|
E415084
|
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: Pollak–Grabert–Hänggi turnover formula | Statement: [Kramers turnover theory, relatedTo, Pollak–Grabert–Hänggi turnover formula]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pollak–Grabert–Hänggi turnover formula Context triple: [Kramers turnover theory, relatedTo, Pollak–Grabert–Hänggi turnover formula]
-
A.
Kramers turnover theory
Kramers turnover theory is a foundational concept in chemical physics that describes how reaction rates depend on friction or solvent viscosity, predicting a maximum (turnover) as friction varies.
-
B.
Landauer–Büttiker formalism
The Landauer–Büttiker formalism is a theoretical framework in mesoscopic physics that describes electrical conductance in terms of quantum transmission of electrons through scattering channels.
-
C.
Einstein–Smoluchowski relation
The Einstein–Smoluchowski relation is a fundamental equation in statistical physics that links the diffusion coefficient of particles undergoing Brownian motion to their mobility and thermal energy.
-
D.
Systems in Stochastic Equilibrium
Systems in Stochastic Equilibrium is a seminal mathematical monograph by Peter Whittle that develops the theory of stochastic processes and their long-run equilibrium behavior in complex systems.
-
E.
Arrhenius equation for temperature dependence of reaction rates
The Arrhenius equation for temperature dependence of reaction rates is a fundamental formula in chemical kinetics that quantitatively relates a reaction’s rate constant to temperature and activation energy, explaining why reactions speed up as temperature increases.
- 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: Pollak–Grabert–Hänggi turnover formula Triple: [Kramers turnover theory, relatedTo, Pollak–Grabert–Hänggi turnover formula]
Generated description
The Pollak–Grabert–Hänggi turnover formula is a refined theoretical expression in chemical physics that quantitatively describes reaction rate behavior across the full range from low to high friction, extending and completing Kramers’ original turnover theory.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Pollak–Grabert–Hänggi turnover formula Target entity description: The Pollak–Grabert–Hänggi turnover formula is a refined theoretical expression in chemical physics that quantitatively describes reaction rate behavior across the full range from low to high friction, extending and completing Kramers’ original turnover theory.
-
A.
Kramers turnover theory
chosen
Kramers turnover theory is a foundational concept in chemical physics that describes how reaction rates depend on friction or solvent viscosity, predicting a maximum (turnover) as friction varies.
-
B.
Landauer–Büttiker formalism
The Landauer–Büttiker formalism is a theoretical framework in mesoscopic physics that describes electrical conductance in terms of quantum transmission of electrons through scattering channels.
-
C.
Einstein–Smoluchowski relation
The Einstein–Smoluchowski relation is a fundamental equation in statistical physics that links the diffusion coefficient of particles undergoing Brownian motion to their mobility and thermal energy.
-
D.
Systems in Stochastic Equilibrium
Systems in Stochastic Equilibrium is a seminal mathematical monograph by Peter Whittle that develops the theory of stochastic processes and their long-run equilibrium behavior in complex systems.
-
E.
Arrhenius equation for temperature dependence of reaction rates
The Arrhenius equation for temperature dependence of reaction rates is a fundamental formula in chemical kinetics that quantitatively relates a reaction’s rate constant to temperature and activation energy, explaining why reactions speed up as temperature increases.
- F. None of above.
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_69d886cfc8e88190b05ba466edd35591 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dc2683fc81908af2df9012addecb |
completed | April 18, 2026, 7:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0139ffbe808190a24e827331ee4a6c |
completed | May 11, 2026, 2:07 a.m. |
| NEDg | Description generation | batch_6a013ae388548190b09d2c81e1ab0d02 |
completed | May 11, 2026, 2:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a013b4df74c81908b3b99e276531e13 |
completed | May 11, 2026, 2:13 a.m. |
Created at: April 10, 2026, 5:35 a.m.