Triple
T12799094
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matilda II infantry tank |
E305966
|
entity |
| Predicate | armourThicknessMin |
P106455
|
FINISHED |
| Object | 20 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: 20 mm | Statement: [Matilda II infantry tank, armourThicknessMin, 20 mm]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: armourThicknessMin Context triple: [Matilda II infantry tank, armourThicknessMin, 20 mm]
-
A.
armourThickness
Indicates the measured thickness of an entity’s protective armor in the context of defense or shielding.
-
B.
sideArmorThickness
Indicates the thickness of an object's armor specifically along its sides.
-
C.
deckArmorThickness
Indicates the thickness of the armor plating on the horizontal deck surface of a vehicle, vessel, or structure.
-
D.
armorThicknessMax
Indicates the maximum thickness of armor that an entity possesses or can withstand.
-
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. 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_69d7bdf366888190a8cccb982606889c |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e6f858c8190915ede38e9a6a2df |
completed | April 10, 2026, 9:41 p.m. |
| PD | Predicate disambiguation | batch_69d9640ed7448190b276e7fab649f7d2 |
completed | April 10, 2026, 8:56 p.m. |
| PDg | Predicate description generation | batch_69d96d88be0481908c311f1e71b61e70 |
completed | April 10, 2026, 9:37 p.m. |
Created at: April 9, 2026, 5:30 p.m.