Triple
T8001028
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | King George V class |
E186247
|
entity |
| Predicate | deckArmourThickness |
P18833
|
FINISHED |
| Object | up to 4 inches |
—
|
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 4 inches | Statement: [King George V class, deckArmourThickness, up to 4 inches]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: deckArmourThickness Context triple: [King George V class, deckArmourThickness, up to 4 inches]
-
A.
deckArmorThickness
chosen
Indicates the thickness of the armor plating on the horizontal deck surface of a vehicle, vessel, or structure.
-
B.
armourThickness
Indicates the measured thickness of an entity’s protective armor in the context of defense or shielding.
-
C.
sideArmorThickness
Indicates the thickness of an object's armor specifically along its sides.
-
D.
frontHullArmorThickness
Indicates the thickness of the armor located on the front section of a vehicle’s hull.
-
E.
armoredBeltThickness
Indicates the thickness of an entity’s protective armored belt in the context of its defensive structure or design.
- 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_69ca82aaaf24819084b94d18f699ba53 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3c9cd06081908b62bb41e228c321 |
completed | March 31, 2026, 3:16 a.m. |
| PD | Predicate disambiguation | batch_69cb0483d3b48190b250c7603d747bca |
completed | March 30, 2026, 11:17 p.m. |
Created at: March 30, 2026, 5:17 p.m.