Triple
T30842979
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leica screw-mount lenses (via adapter on M bodies) |
E785559
|
entity |
| Predicate | adapterTypesInclude |
P173757
|
FINISHED |
| Object | 28mm frameline adapter |
—
|
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: 28mm frameline adapter | Statement: [Leica screw-mount lenses (via adapter on M bodies), adapterTypesInclude, 28mm frameline adapter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: adapterTypesInclude Context triple: [Leica screw-mount lenses (via adapter on M bodies), adapterTypesInclude, 28mm frameline adapter]
-
A.
includesDevices
Indicates that one entity contains, encompasses, or has as part of it one or more devices.
-
B.
adapterMustProvide
Indicates that an adapter is required to supply or implement a specified functionality, interface, or service.
-
C.
includesFeatureTypes
Indicates that an entity contains or encompasses specific types of features as part of its composition or definition.
-
D.
supportsDetectorTypes
Indicates that an entity is compatible with and can operate using specific types or categories of detectors.
-
E.
supportedEquipment
Indicates that one entity provides compatibility with, or operational support for, a specified piece of equipment.
- 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_69f224b850848190a4af4ccf8ddadcdf |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f6ba1733408190af579d93a7946508 |
completed | May 3, 2026, 2:59 a.m. |
| PD | Predicate disambiguation | batch_69f6b6293188819080d5041ca0adb969 |
completed | May 3, 2026, 2:42 a.m. |
| PDg | Predicate description generation | batch_69f6b960ca4081909a77690c2b122f5e |
completed | May 3, 2026, 2:56 a.m. |
Created at: April 29, 2026, 8:45 p.m.