Triple
T762191
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yamato |
E16094
|
entity |
| Predicate | armorBeltThickness |
P9690
|
FINISHED |
| Object | up to about 410 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 410 mm | Statement: [Yamato, armorBeltThickness, up to about 410 mm]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: armorBeltThickness Context triple: [Yamato, armorBeltThickness, up to about 410 mm]
-
A.
armorBelt
Indicates that an entity is equipped with or wearing an armor belt as part of its protective gear.
-
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.
armorType
Indicates the specific category or classification of protective armor associated with an entity.
-
D.
armour
Indicates that an entity provides protective covering or defense for another entity.
-
E.
thickness
chosen
Indicates the measure of how deep or wide an object or layer is from one surface or side to its opposite.
- 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_69a493684ee48190bd43b7c78da4aec8 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a6841f388190a6d08c3bf5c17fe4 |
completed | March 1, 2026, 8:50 p.m. |
| PD | Predicate disambiguation | batch_69a4a5048a8081908d0542214142664a |
completed | March 1, 2026, 8:43 p.m. |
Created at: March 1, 2026, 7:37 p.m.