Triple
T22600997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cahn–Hilliard equation |
E574814
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | Allen–Cahn equation |
—
|
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: Allen–Cahn equation | Statement: [Cahn–Hilliard equation, relatedTo, Allen–Cahn equation]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Allen–Cahn equation Context triple: [Cahn–Hilliard equation, relatedTo, Allen–Cahn equation]
-
A.
Cahn–Hilliard equation
The Cahn–Hilliard equation is a nonlinear partial differential equation that models phase separation and coarsening in binary mixtures and other systems undergoing spinodal decomposition.
-
B.
Mullins–Sekerka instability
The Mullins–Sekerka instability is a morphological instability that occurs during diffusion-limited solidification or crystal growth, leading to pattern formation such as dendrites at moving phase boundaries.
-
C.
Stefan problem
The Stefan problem is a classical mathematical model in heat transfer that describes how phase-change boundaries, such as the interface between ice and water, move over time.
-
D.
Monge–Ampère equation
The Monge–Ampère equation is a fully nonlinear partial differential equation central to differential geometry, optimal transport, and several complex variables, often used to study curvature and geometric structures.
-
E.
Kardar–Parisi–Zhang equation
The Kardar–Parisi–Zhang equation is a fundamental stochastic partial differential equation that models the dynamic scaling and roughening of growing interfaces in nonequilibrium statistical physics.
- 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: Allen–Cahn equation Target entity description: The Allen–Cahn equation is a reaction–diffusion partial differential equation used to model phase separation and interface motion in multi-phase systems.
-
A.
Cahn–Hilliard equation
The Cahn–Hilliard equation is a nonlinear partial differential equation that models phase separation and coarsening in binary mixtures and other systems undergoing spinodal decomposition.
-
B.
Mullins–Sekerka instability
The Mullins–Sekerka instability is a morphological instability that occurs during diffusion-limited solidification or crystal growth, leading to pattern formation such as dendrites at moving phase boundaries.
-
C.
Stefan problem
The Stefan problem is a classical mathematical model in heat transfer that describes how phase-change boundaries, such as the interface between ice and water, move over time.
-
D.
Monge–Ampère equation
The Monge–Ampère equation is a fully nonlinear partial differential equation central to differential geometry, optimal transport, and several complex variables, often used to study curvature and geometric structures.
-
E.
Kardar–Parisi–Zhang equation
The Kardar–Parisi–Zhang equation is a fundamental stochastic partial differential equation that models the dynamic scaling and roughening of growing interfaces in nonequilibrium statistical physics.
- 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_69e245bc11308190b69d794d5d1e0bb6 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1626c6ce08190b991e89b12c67a5a |
completed | April 29, 2026, 1:44 a.m. |
Created at: April 17, 2026, 2:50 p.m.