Triple
T26244326
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Talbot effect |
E656399
|
entity |
| Predicate | TalbotLengthFormula |
P160398
|
FINISHED |
| Object | z_T = 2 a^2 / λ for a 1D grating in 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: z_T = 2 a^2 / λ for a 1D grating in paraxial approximation | Statement: [Talbot effect, TalbotLengthFormula, z_T = 2 a^2 / λ for a 1D grating in paraxial approximation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: TalbotLengthFormula Context triple: [Talbot effect, TalbotLengthFormula, z_T = 2 a^2 / λ for a 1D grating in paraxial approximation]
-
A.
tubeLengthRelativeToFocalLength
Indicates the relationship between an optical system’s tube length and its focal length, typically expressing how long the tube is relative to the focal length.
-
B.
typicalThicknessFormula
Indicates the standard or commonly used formula for calculating the thickness of something under typical conditions.
-
C.
dimensionOfLength
Indicates that something represents or specifies a measurement along a single spatial extent (a length dimension).
-
D.
telescopeLength
Indicates the physical length measurement of a telescope.
-
E.
totalLength_m
Indicates the overall measured length of something expressed in meters.
- 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_69ee5b4c59a881909d9ee4fd013fffd5 |
completed | April 26, 2026, 6:37 p.m. |
| NER | Named-entity recognition | batch_69f60dc4aa808190ba3b5682944a7fb8 |
completed | May 2, 2026, 2:44 p.m. |
| PD | Predicate disambiguation | batch_69f5f7fd90fc81909055b211368f9139 |
completed | May 2, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69f600be0de88190989611e952b03117 |
completed | May 2, 2026, 1:48 p.m. |
Created at: April 26, 2026, 9:04 p.m.