Triple
T11462641
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hitler’s buzzsaw |
E271698
|
entity |
| Predicate | refersToWeaponType |
P67648
|
FINISHED |
| Object | belt-fed machine gun |
—
|
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: belt-fed machine gun | Statement: [Hitler’s buzzsaw, refersToWeaponType, belt-fed machine gun]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: refersToWeaponType Context triple: [Hitler’s buzzsaw, refersToWeaponType, belt-fed machine gun]
-
A.
relatedToWeaponType
chosen
Indicates that an entity has an association or connection with a specific type or category of weapon.
-
B.
hasWeaponType
Indicates that an entity is associated with or equipped with a specific type or category of weapon.
-
C.
weaponTypeTested
Indicates that a specific type of weapon has been subjected to a test or evaluation in the described context.
-
D.
typicalWeapon
Indicates that the object is a weapon commonly or characteristically used by the subject.
-
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_69d6aae0c8d881908a5a360c0be3242e |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d822f488248190b9f603cd31c72174 |
completed | April 9, 2026, 10:06 p.m. |
| PD | Predicate disambiguation | batch_69d80867ff248190bb157fa9e355353b |
completed | April 9, 2026, 8:13 p.m. |
Created at: April 8, 2026, 9:35 p.m.