Triple
T14694301
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Derfflinger class |
E345112
|
entity |
| Predicate | armourTurretFaceThickness |
P27154
|
FINISHED |
| Object | up to 270 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 270 mm | Statement: [Derfflinger class, armourTurretFaceThickness, up to 270 mm]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: armourTurretFaceThickness Context triple: [Derfflinger class, armourTurretFaceThickness, up to 270 mm]
-
A.
armorTurretFaceThickness
chosen
Indicates the thickness of the armor on the front-facing surface of a turret.
-
B.
armourThickness
Indicates the measured thickness of an entity’s protective armor in the context of defense or shielding.
-
C.
frontHullArmorThickness
Indicates the thickness of the armor located on the front section of a vehicle’s hull.
-
D.
armorThicknessMax
Indicates the maximum thickness of armor that an entity possesses or can withstand.
-
E.
sideArmorThickness
Indicates the thickness of an object's armor specifically along its sides.
- 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_69d822e34b348190ada4d1cdb6c7c226 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb586e7108190be644db9cf9a4d99 |
completed | April 14, 2026, 9:45 p.m. |
| PD | Predicate disambiguation | batch_69de657c57ec8190ae0b9bb79a514566 |
completed | April 14, 2026, 4:04 p.m. |
Created at: April 10, 2026, 1:28 a.m.