Triple
T12426338
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lieutenant Marion "Cobra" Cobretti |
E296906
|
entity |
| Predicate | weaponCaliber |
P6076
|
FINISHED |
| Object | .45 ACP |
—
|
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: .45 ACP | Statement: [Lieutenant Marion "Cobra" Cobretti, weaponCaliber, .45 ACP]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: weaponCaliber Context triple: [Lieutenant Marion "Cobra" Cobretti, weaponCaliber, .45 ACP]
-
A.
gunCalibre
chosen
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.
natoCaliberClass
Indicates that two ammunition types share the same standardized NATO caliber classification.
-
D.
ammunitionCapacity
Indicates the maximum amount of ammunition that something (typically a weapon or container) is designed to hold at one time.
-
E.
lightArmament
Indicates that an entity is equipped with or characterized by relatively minimal or lightweight weaponry compared to standard or heavy armament.
- 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_69d6ada0640c81908c061d7fb3d47786 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94df948308190ace333230a4a3b38 |
completed | April 10, 2026, 7:22 p.m. |
| PD | Predicate disambiguation | batch_69d94d391c548190996a8c698357f273 |
completed | April 10, 2026, 7:19 p.m. |
Created at: April 8, 2026, 9:55 p.m.