Triple
T28309698
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pattern 1853 socket bayonet |
E713961
|
entity |
| Predicate | associatedWithFirearmType |
P67648
|
FINISHED |
| Object | rifle-musket |
—
|
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: rifle-musket | Statement: [Pattern 1853 socket bayonet, associatedWithFirearmType, rifle-musket]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithFirearmType Context triple: [Pattern 1853 socket bayonet, associatedWithFirearmType, rifle-musket]
-
A.
associatedWithWeapon
Indicates that an entity has a connection or involvement with a weapon, such as ownership, use, presence, or relevance in a given context.
-
B.
prohibitedWeaponType
Indicates that a particular type of weapon is classified as not allowed or forbidden under specified rules, laws, or agreements.
-
C.
hasFirearms
Indicates that an entity possesses, controls, or is equipped with one or more firearms.
-
D.
relatedToWeaponType
chosen
Indicates that an entity has an association or connection with a specific type or category of weapon.
-
E.
gunType
Indicates the specific category or kind of gun associated with an entity.
- 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_69efb5256afc8190b9322d25c3ae6320 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_6a013632a3048190b05716d803f34716 |
completed | May 11, 2026, 1:51 a.m. |
| PD | Predicate disambiguation | batch_6a01309582e48190a05d47d96ffb7a46 |
completed | May 11, 2026, 1:27 a.m. |
Created at: April 27, 2026, 11:39 p.m.