Triple
T36835744
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | VK 45.01 (H) |
E910265
|
entity |
| Predicate | designedFrontArmourThickness |
P199866
|
FINISHED |
| Object | over 100 mm (conceptual) |
—
|
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: over 100 mm (conceptual) | Statement: [VK 45.01 (H), designedFrontArmourThickness, over 100 mm (conceptual)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: designedFrontArmourThickness Context triple: [VK 45.01 (H), designedFrontArmourThickness, over 100 mm (conceptual)]
-
A.
armourThickness
Indicates the measured thickness of an entity’s protective armor in the context of defense or shielding.
-
B.
armourThicknessMin
Indicates the minimum measured or specified thickness of an object's armor in the described context.
-
C.
deckArmorThickness
Indicates the thickness of the armor plating on the horizontal deck surface of a vehicle, vessel, or structure.
-
D.
sideArmorThickness
Indicates the thickness of an object's armor specifically along its sides.
-
E.
superstructureFrontArmorThickness
Indicates the thickness of the armor located on the front-facing portion of a vehicle’s superstructure.
- F. None of above. chosen
Provenance (4 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_69f76e7e9d60819092442fba73290a46 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69ff5f5ecc808190b2df364da108ff4c |
completed | May 9, 2026, 4:22 p.m. |
| PD | Predicate disambiguation | batch_69ff5b84131c8190bf81d7fb53e934bc |
completed | May 9, 2026, 4:06 p.m. |
| PDg | Predicate description generation | batch_69ff5f5ddfcc819092563419f9e82d60 |
completed | May 9, 2026, 4:22 p.m. |
Created at: May 3, 2026, 4:13 p.m.