Triple
T32807570
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NX Nastran |
E839058
|
entity |
| Predicate | supportsMaterialModel |
P19966
|
FINISHED |
| Object | linear elastic materials |
—
|
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: linear elastic materials | Statement: [NX Nastran, supportsMaterialModel, linear elastic materials]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: supportsMaterialModel Context triple: [NX Nastran, supportsMaterialModel, linear elastic materials]
-
A.
supportsMaterial
Indicates that one entity provides structural or functional support to a material entity, enabling it to be held, stabilized, or borne.
-
B.
usesSupportMaterial
Indicates that an entity relies on or incorporates additional supporting materials or resources to carry out an action or fulfill a function.
-
C.
supportedModel
Indicates that one entity provides compatibility with, or operational backing for, a particular model.
-
D.
supportsModelingOf
Indicates that one entity provides the capability or functionality needed to represent, simulate, or model another entity or process.
-
E.
supportsModelType
chosen
Indicates that an entity is compatible with, or can operate using, a specified model type.
- 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_69f3493d35208190b4351b4e85f2fa16 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a037cad051c8190b28b354b89208574 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a0379f0cbe481909b4b8fc6cbe297f0 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 1:15 a.m.