Triple
T10340772
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Landau levels |
E243121
|
entity |
| Predicate | areSolutionOf |
P14252
|
FINISHED |
| Object | Schrödinger equation in a uniform magnetic field |
—
|
LITERAL FINISHED |
How this triple was built (2 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: Schrödinger equation in a uniform magnetic field | Statement: [Landau levels, areSolutionOf, Schrödinger equation in a uniform magnetic field]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: areSolutionOf Context triple: [Landau levels, areSolutionOf, Schrödinger equation in a uniform magnetic field]
-
A.
isSolutionOf
chosen
Indicates that one entity is a correct answer or satisfies the conditions of a given problem, equation, or task.
-
B.
admitsSolution
Indicates that a problem, system, or situation allows for or possesses at least one valid solution.
-
C.
providesSolutionFor
Indicates that one entity offers or supplies a remedy, answer, or resolution to a problem, need, or issue associated with another entity.
-
D.
basedOnSolutionOf
Indicates that one entity is derived, developed, or constructed using the solution or outcome produced by another entity.
-
E.
solutionType
Indicates the specific category or kind of solution associated with an entity or problem.
- F. None of above.
Provenance (3 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_69d381af787481908bc401325c760a88 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e91fdb2081909866c6ecf417d75a |
completed | April 7, 2026, 11:23 a.m. |
| PD | Predicate disambiguation | batch_69d4df9dc3208190bf1bd106f44f6202 |
completed | April 7, 2026, 10:42 a.m. |
Created at: April 6, 2026, 11:55 a.m.