Triple
T25726118
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Quantum ESPRESSO |
E645119
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | density-functional theory code |
C2539
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: density-functional theory code Context triple: [Quantum ESPRESSO, instanceOf, density-functional theory code]
-
A.
molecular quantum mechanics method
A molecular quantum mechanics method is a theoretical and computational approach that applies quantum mechanical principles to describe and predict the electronic structure, properties, and behavior of molecules.
-
B.
materials informatics software
Materials informatics software is a computational platform that integrates materials data, machine learning, and simulation tools to accelerate the discovery, design, and optimization of materials and their properties.
-
C.
technical code
chosen
Technical code is a structured set of machine-readable instructions, written in a specific programming language, that implements precise computational logic and system behavior according to defined technical requirements.
-
D.
building code
A building code is a set of regulations and standards that govern the design, construction, alteration, and maintenance of structures to ensure safety, health, accessibility, and energy efficiency.
-
E.
scientific computing reference
A scientific computing reference is a comprehensive resource that provides definitions, formulas, algorithms, and best practices for performing numerical analysis, data processing, and computational modeling in scientific and engineering contexts.
- F. None of above.
Provenance (1 batch)
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_69e77e85254081908d79ee4e8715f283 |
completed | April 21, 2026, 1:41 p.m. |
Created at: April 21, 2026, 11:04 p.m.