Triple
T13594067
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nariakira Arisaka |
E324768
|
entity |
| Predicate | designedWeaponType |
P16410
|
FINISHED |
| Object | bolt-action rifle |
—
|
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: bolt-action rifle | Statement: [Nariakira Arisaka, designedWeaponType, bolt-action rifle]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: designedWeaponType Context triple: [Nariakira Arisaka, designedWeaponType, bolt-action rifle]
-
A.
weaponDesigner
Indicates that one entity is the creator or designer of a weapon used or associated with another entity.
-
B.
typicalWeapon
Indicates that the object is a weapon commonly or characteristically used by the subject.
-
C.
weaponCategory
chosen
Indicates the classification or type of weapon to which an item or armament belongs.
-
D.
weaponTypeTested
Indicates that a specific type of weapon has been subjected to a test or evaluation in the described context.
-
E.
involvesWeaponType
Indicates that the relationship or action includes the use, presence, or association of a specific type or category of weapon.
- 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_69d80769eaf081909d82f44e484d6113 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb057f1c881909a3bb77c659a724a |
completed | April 12, 2026, 2:46 p.m. |
| PD | Predicate disambiguation | batch_69dbae18eaf48190809e8b365856cde9 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:49 p.m.