Triple
T7570498
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rayleigh scattering |
E179225
|
entity |
| Predicate | crossSectionProportionalTo |
P9147
|
FINISHED |
| Object | 1/λ^4 |
—
|
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/λ^4 | Statement: [Rayleigh scattering, crossSectionProportionalTo, 1/λ^4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: crossSectionProportionalTo Context triple: [Rayleigh scattering, crossSectionProportionalTo, 1/λ^4]
-
A.
proportionalTo
Indicates that one quantity varies in constant ratio to another, so changes in one are directly reflected by proportional changes in the other.
-
B.
isProportionalTo
chosen
Indicates that one quantity varies in constant ratio to another, so when one changes, the other changes by a fixed multiplicative factor.
-
C.
isProportionalityFactorIn
Indicates that one quantity serves as the proportionality factor (constant of proportionality) in a specified proportional relationship or equation.
-
D.
hasCrossSection
Indicates that one entity represents or possesses the cross-sectional shape, profile, or slice of another entity.
-
E.
hasProportion
Indicates that one entity stands in a specified ratio, fraction, or relative share to another entity or whole.
- 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_69c69f316e50819081a271c85c06f918 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f920540c8190817712db5aa3eeff |
completed | March 27, 2026, 9:39 p.m. |
| PD | Predicate disambiguation | batch_69c6f4de77048190b8769e717fdcf8e7 |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:51 p.m.