Triple
T24014365
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reynolds-averaged Navier–Stokes turbulence modeling |
E594627
|
entity |
| Predicate | requiresModelingOf |
P154870
|
FINISHED |
| Object | Reynolds stress tensor |
—
|
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: Reynolds stress tensor | Statement: [Reynolds-averaged Navier–Stokes turbulence modeling, requiresModelingOf, Reynolds stress tensor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: requiresModelingOf Context triple: [Reynolds-averaged Navier–Stokes turbulence modeling, requiresModelingOf, Reynolds stress tensor]
-
A.
supportsModelingOf
Indicates that one entity provides the capability or functionality needed to represent, simulate, or model another entity or process.
-
B.
requiresFineTuningOf
Indicates that one entity needs the adjustment, calibration, or refinement of another entity in order to function correctly or optimally.
-
C.
modeledWith
Indicates that something is represented, simulated, or described using a particular model, method, or modeling technique.
-
D.
requiresDetectionOf
Indicates that one entity can only occur, be valid, or proceed if another entity has first been detected or identified.
-
E.
usesModelsType
Indicates that one entity employs or relies on a specific type or category of models in its operation or behavior.
- F. None of above. chosen
Provenance (4 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_69e288bc8f608190ac4af29f0bd1c744 |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1d5a0aa408190a27fca07777cda05 |
completed | April 29, 2026, 9:55 a.m. |
| PD | Predicate disambiguation | batch_69f17639d23c8190bed93434e2f9230a |
completed | April 29, 2026, 3:08 a.m. |
| PDg | Predicate description generation | batch_69f17c28b684819084eea522126463f8 |
completed | April 29, 2026, 3:34 a.m. |
Created at: April 17, 2026, 9:42 p.m.