Triple
T1080729
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fresnel diffraction theory |
E23939
|
entity |
| Predicate | usesApproximation |
P4447
|
FINISHED |
| Object | paraxial approximation |
—
|
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: paraxial approximation | Statement: [Fresnel diffraction theory, usesApproximation, paraxial approximation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesApproximation Context triple: [Fresnel diffraction theory, usesApproximation, paraxial approximation]
-
A.
approximates
Indicates that one entity is close to, but not exactly equal to, the value, form, or behavior of another entity.
-
B.
approximationType
chosen
Indicates the specific method or scheme used to approximate a value, function, or relationship in a given context.
-
C.
hasApproximateValue
Indicates that one entity’s value is close to, but not exactly equal to, the value of another entity within an acceptable margin of error.
-
D.
hasDimensionsApprox
Indicates that an entity has physical dimensions that are known only approximately, rather than as exact measurements.
-
E.
usesSamplingOf
Indicates that one entity employs or relies on a sample or subset derived from another entity for its operation, analysis, or processing.
- 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_69a493f1ddf48190a99d54b00e99f8ce |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b946af788190b400644a2dec68c3 |
completed | March 1, 2026, 10:10 p.m. |
| PD | Predicate disambiguation | batch_69a4b73d9f08819093668104f129840e |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:42 p.m.