Triple
T4441588
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barlow lens |
E95782
|
entity |
| Predicate | typicalMagnificationFactor |
P49706
|
FINISHED |
| Object | 2x |
—
|
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: 2x | Statement: [Barlow lens, typicalMagnificationFactor, 2x]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalMagnificationFactor Context triple: [Barlow lens, typicalMagnificationFactor, 2x]
-
A.
typicalMagnification
chosen
Indicates the usual or characteristic degree to which something is enlarged or magnified under normal or standard conditions.
-
B.
magnitudeScale
Indicates the scale or measurement system used to quantify the magnitude or intensity of something.
-
C.
typicalDampingFactor
Indicates the usual or characteristic level of damping applied in a system or process, describing how strongly motion or oscillations are typically reduced.
-
D.
focalLength
Indicates the distance between a lens or mirror and its focal point, determining how strongly it converges or diverges light.
-
E.
transmissionFactor
Indicates how strongly or efficiently something is passed or transmitted from one entity to another.
- 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_69b3453ea2b48190a26f154b3b8fece5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b355ad71588190b1dcad4250472c29 |
completed | March 13, 2026, 12:09 a.m. |
| PD | Predicate disambiguation | batch_69b34f62c180819097ced38da2052207 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:32 p.m.