Triple
T13021075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 6.5×50mmSR Arisaka |
E326170
|
entity |
| Predicate | standardBulletWeight |
P107501
|
FINISHED |
| Object | approximately 139 grains |
—
|
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: approximately 139 grains | Statement: [6.5×50mmSR Arisaka, standardBulletWeight, approximately 139 grains]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: standardBulletWeight Context triple: [6.5×50mmSR Arisaka, standardBulletWeight, approximately 139 grains]
-
A.
gunCalibre
Indicates the relationship between a firearm and the calibre (size/diameter) of ammunition it is designed to use.
-
B.
ammunitionType
Indicates the specific kind or category of ammunition associated with or used by an entity.
-
C.
rangeStandardAmmunition
Indicates that the relationship specifies the standard type of ammunition used for a given range or weapon system.
-
D.
munitionWeightCategory
Indicates the classification of a munition based on its weight range or weight-related category.
-
E.
hasHeavierBarrelThan
Indicates that the barrel of one entity has a greater weight than the barrel of another entity.
- 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_69d8076cc45c81908123123f43e69266 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97ecf21bc819082fb512bc479b4be |
completed | April 10, 2026, 10:50 p.m. |
| PD | Predicate disambiguation | batch_69d97dc39a0881908119c62e31bf6182 |
completed | April 10, 2026, 10:46 p.m. |
| PDg | Predicate description generation | batch_69d97e3df2288190a7f27d31d248bb7f |
completed | April 10, 2026, 10:48 p.m. |
Created at: April 9, 2026, 8:52 p.m.