Triple
T17752835
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gaussian orthogonal ensemble |
E443153
|
entity |
| Predicate | levelRepulsionExponent |
P128210
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [Gaussian orthogonal ensemble, levelRepulsionExponent, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: levelRepulsionExponent Context triple: [Gaussian orthogonal ensemble, levelRepulsionExponent, 1]
-
A.
axisForcesStrength
Indicates the magnitude or intensity of forces acting along a specified axis.
-
B.
typicalDampingFactor
Indicates the usual or characteristic level of damping applied in a system or process, describing how strongly motion or oscillations are typically reduced.
-
C.
temperatureDependenceExponent
Indicates how strongly a quantity or process changes in response to variations in temperature, typically expressed as an exponent in a temperature-dependent relationship.
-
D.
energyScaleBehavior
Indicates how a quantity or system changes or responds when the overall energy scale of the situation is varied.
-
E.
enforcementStrength
Indicates the degree or intensity with which rules, laws, or policies are applied and enforced in a given context.
- 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_69d8b9edf16c8190a59ebd245d378f4f |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4841c0540819093a32d759775c61f |
completed | April 19, 2026, 7:28 a.m. |
| PD | Predicate disambiguation | batch_69e3cde9dc288190af0e2198487f2051 |
completed | April 18, 2026, 6:31 p.m. |
| PDg | Predicate description generation | batch_69e3cfab7edc8190b663282d565a0389 |
completed | April 18, 2026, 6:38 p.m. |
Created at: April 10, 2026, 10:10 a.m.