Triple
T37659024
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Distagon lens design |
E937671
|
entity |
| Predicate | hasOpticalFeature |
P194048
|
FINISHED |
| Object | large back focal distance |
—
|
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: large back focal distance | Statement: [Distagon lens design, hasOpticalFeature, large back focal distance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOpticalFeature Context triple: [Distagon lens design, hasOpticalFeature, large back focal distance]
-
A.
hasOpticalElement
Indicates that one entity includes, contains, or is equipped with a specific optical element as a component or part.
-
B.
hasOpticalChannels
Indicates that an entity possesses one or more optical communication or signal-transmission channels.
-
C.
opticalTechnology
Indicates that one entity employs or involves optical methods, devices, or systems as its primary technology.
-
D.
hasOpticalImageStabilization
Indicates that a device or component includes a feature that reduces image blur caused by camera movement during capture.
-
E.
usesOpticsType
Indicates that one entity employs or is characterized by a specific type of optical system or technology.
- 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_69f76ed6df7c8190b018e5baea716ceb |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fd5d48855c8190bd93070b6a00d8b5 |
completed | May 8, 2026, 3:49 a.m. |
| PD | Predicate disambiguation | batch_69fd5c9aabb88190912800d90184a89d |
completed | May 8, 2026, 3:46 a.m. |
| PDg | Predicate description generation | batch_69fd5d47da488190a4f2dbd44a0a83b2 |
completed | May 8, 2026, 3:49 a.m. |
Created at: May 3, 2026, 4:18 p.m.