Triple
T2685244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | USS South Dakota (BB-57) |
E57468
|
entity |
| Predicate | armorDeckMaxThickness |
P18833
|
FINISHED |
| Object | 6 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: 6 inches | Statement: [USS South Dakota (BB-57), armorDeckMaxThickness, 6 inches]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: armorDeckMaxThickness Context triple: [USS South Dakota (BB-57), armorDeckMaxThickness, 6 inches]
-
A.
deckArmorThickness
chosen
Indicates the thickness of the armor plating on the horizontal deck surface of a vehicle, vessel, or structure.
-
B.
armoredBeltThickness
Indicates the thickness of an entity’s protective armored belt in the context of its defensive structure or design.
-
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.
armorTurretFaceThickness
Indicates the thickness of the armor on the front-facing surface of a turret.
- 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_69ab4a5028388190a36f3baf1588309e |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd9edba5c8190b86d6cba0f1964e2 |
completed | March 7, 2026, 7:55 a.m. |
| PD | Predicate disambiguation | batch_69abd81c9b4c81908e5e0da6ac5f828b |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:54 p.m.