Triple
T314068
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leopard 2 |
E7668
|
entity |
| Predicate | lengthWithGunForward |
P266
|
FINISHED |
| Object | approximately 9.97 m |
—
|
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: approximately 9.97 m | Statement: [Leopard 2, lengthWithGunForward, approximately 9.97 m]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lengthWithGunForward Context triple: [Leopard 2, lengthWithGunForward, approximately 9.97 m]
-
A.
gunCalibre
Indicates the relationship between a firearm and the calibre (size/diameter) of ammunition it is designed to use.
-
B.
gunType
Indicates the specific category or kind of gun associated with an entity.
-
C.
gunStabilisation
Indicates that an entity performs or provides stabilization for a gun, reducing its movement or recoil to improve accuracy.
-
D.
gun
Indicates that one entity uses, carries, or is associated with a gun in relation to another entity or context.
-
E.
length
chosen
Indicates a measurement relationship where a value specifies how long something is from one end to the other.
- 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_69a2e7e7af7881908890039d6be4e9b8 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ea4bd6a081909bdb57602c7093b4 |
completed | Feb. 28, 2026, 1:14 p.m. |
| PD | Predicate disambiguation | batch_69a2e9428098819089d5950cd2c96dc4 |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:07 p.m.