Triple
T4133803
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mini-Beryl |
E85101
|
entity |
| Predicate | sightMounting |
P54047
|
FINISHED |
| Object | side rail for optics (in some variants) |
—
|
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: side rail for optics (in some variants) | Statement: [Mini-Beryl, sightMounting, side rail for optics (in some variants)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sightMounting Context triple: [Mini-Beryl, sightMounting, side rail for optics (in some variants)]
-
A.
typeOfGunMount
Indicates the specific kind or configuration of gun mounting used to support or attach a gun.
-
B.
bayonetMount
Indicates that one object is equipped with or designed to accept a bayonet-style mounting connection to another object.
-
C.
usesLensMount
Indicates that one device or component is designed to accept, attach to, or operate with a specific type of lens mount.
-
D.
gunStabilisation
Indicates that an entity performs or provides stabilization for a gun, reducing its movement or recoil to improve accuracy.
-
E.
sightType
Indicates the specific kind or category of sight or visual perception associated with an entity or event.
- 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_69aed935ccd881909dc61f81bcdb7a78 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af03a0f3408190adba7a8513bd3d12 |
completed | March 9, 2026, 5:30 p.m. |
| PD | Predicate disambiguation | batch_69af01883b6c8190a482ead589a131a5 |
completed | March 9, 2026, 5:21 p.m. |
| PDg | Predicate description generation | batch_69af039fb19c8190b20e62a3b3ad25c1 |
completed | March 9, 2026, 5:30 p.m. |
Created at: March 9, 2026, 3:43 p.m.