Triple
T8729779
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pionierpanzer 2 |
E207222
|
entity |
| Predicate | hasMachineGun |
P34749
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Pionierpanzer 2, hasMachineGun, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMachineGun Context triple: [Pionierpanzer 2, hasMachineGun, yes]
-
A.
hasMuzzle
Indicates that one entity possesses or is equipped with a muzzle, typically as a restraining or protective device.
-
B.
hasChamber
Indicates that one entity possesses, contains, or is associated with a distinct enclosed space or compartment (a chamber).
-
C.
hasSmallCalibreGuns
chosen
Indicates that the subject is equipped with or possesses guns of relatively small calibre compared to standard or typical armaments.
-
D.
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.
-
E.
gun
Indicates that one entity uses, carries, or is associated with a gun in relation to another entity or context.
- 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_69ca8358e4008190898471a59b96c301 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5d19fdc88190860e0c9c93ab79ce |
completed | March 31, 2026, 11:47 p.m. |
| PD | Predicate disambiguation | batch_69cc457093188190959287a6458651c6 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:37 p.m.