Triple
T22666553
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Poisson’s ratio |
E559806
|
entity |
| Predicate | typicalRangeForIsotropicSolids |
P13474
|
FINISHED |
| Object | 0 to 0.5 |
—
|
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: 0 to 0.5 | Statement: [Poisson’s ratio, typicalRangeForIsotropicSolids, 0 to 0.5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalRangeForIsotropicSolids Context triple: [Poisson’s ratio, typicalRangeForIsotropicSolids, 0 to 0.5]
-
A.
mechanicalProperty
Indicates a relationship where a mechanical characteristic (such as strength, stiffness, hardness, or elasticity) is attributed to or associated with an entity.
-
B.
thermalExpansionCoefficient
Indicates how much a material's size changes per unit length (or volume) for each degree change in temperature.
-
C.
typicalRange
chosen
Indicates the usual or expected range of values, conditions, or states within which something normally occurs or applies.
-
D.
thermalConductivity
Indicates how effectively heat is conducted through a material per unit temperature gradient.
-
E.
typicalValueForSteel
Indicates that a given value represents a standard or commonly expected value associated with steel under typical conditions.
- 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_69e2454a158c819093b8e35f5045efb6 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1781c2c808190baf6964ca1eced6f |
completed | April 29, 2026, 3:16 a.m. |
| PD | Predicate disambiguation | batch_69ee62a6245881909506ff502da14137 |
completed | April 26, 2026, 7:08 p.m. |
Created at: April 17, 2026, 3:09 p.m.