Triple
T7684692
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Springfield Model 1861 rifle-musket |
E174084
|
entity |
| Predicate | bayonetModel |
P37995
|
FINISHED |
| Object | Model 1855 socket bayonet |
—
|
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: Model 1855 socket bayonet | Statement: [Springfield Model 1861 rifle-musket, bayonetModel, Model 1855 socket bayonet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bayonetModel Context triple: [Springfield Model 1861 rifle-musket, bayonetModel, Model 1855 socket bayonet]
-
A.
bayonetMount
chosen
Indicates that one object is equipped with or designed to accept a bayonet-style mounting connection to another object.
-
B.
weaponLength
Indicates the length or size of a weapon associated with an entity.
-
C.
Berdan No.1 rifle
Indicates that an entity is a Berdan No.1 rifle, i.e., it has the identity or classification of that specific rifle model.
-
D.
bladeType
Indicates the specific kind or category of blade associated with an object or entity.
-
E.
typicalWeapon
Indicates that the object is a weapon commonly or characteristically used by the subject.
- 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_69c6995840408190a19de6c51090f46f |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7048b0b448190889bd40e0a38e51a |
completed | March 27, 2026, 10:28 p.m. |
| PD | Predicate disambiguation | batch_69c701618d3481908be84b76f36ac5a1 |
completed | March 27, 2026, 10:14 p.m. |
Created at: March 27, 2026, 4:02 p.m.