Triple
T19824153
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Japanese battleship Tosa |
E476274
|
entity |
| Predicate | designedArmorBeltThickness |
P38465
|
FINISHED |
| Object | up to about 280 mm |
—
|
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: up to about 280 mm | Statement: [Japanese battleship Tosa, designedArmorBeltThickness, up to about 280 mm]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: designedArmorBeltThickness Context triple: [Japanese battleship Tosa, designedArmorBeltThickness, up to about 280 mm]
-
A.
armoredBeltThickness
chosen
Indicates the thickness of an entity’s protective armored belt in the context of its defensive structure or design.
-
B.
armourBelt
Indicates a relationship where an armour belt is equipped on, attached to, or associated with an entity (such as a character, vehicle, or structure) as protective gear.
-
C.
sideArmorThickness
Indicates the thickness of an object's armor specifically along its sides.
-
D.
deckArmorThickness
Indicates the thickness of the armor plating on the horizontal deck surface of a vehicle, vessel, or structure.
-
E.
armourThickness
Indicates the measured thickness of an entity’s protective armor in the context of defense or shielding.
- 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_69d8e51c7c188190b926f3a2a7b5f881 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e655017c188190ae9e17ae6b0eee05 |
completed | April 20, 2026, 4:32 p.m. |
| PD | Predicate disambiguation | batch_69e5305bda388190a23b7191768107b1 |
completed | April 19, 2026, 7:43 p.m. |
Created at: April 10, 2026, 1:50 p.m.