Triple
T14023209
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 空母「赤城」 |
E337386
|
entity |
| Predicate | 搭載兵装 |
P49731
|
FINISHED |
| Object | 対空砲 |
—
|
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: 対空砲 | Statement: [空母「赤城」, 搭載兵装, 対空砲]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 搭載兵装 Context triple: [空母「赤城」, 搭載兵装, 対空砲]
-
A.
missionEquipment
Indicates that certain equipment is assigned to, used for, or associated with carrying out a specific mission.
-
B.
weaponMount
Indicates that one entity serves as a mounting point or support structure for attaching or holding a weapon on another entity.
-
C.
armedBy
chosen
Indicates that one entity is supplied with weapons, equipment, or armaments by another entity.
-
D.
loadoutSlot
Indicates the specific equipment or item slot in which a particular piece of gear or object is placed within a loadout configuration.
-
E.
equippedFor
Indicates that one entity is suitably provided with the necessary tools, features, or capabilities to perform a particular function or handle a specific situation for another entity or purpose.
- 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_69d81c6543a48190bd5ba93d7419e797 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2f3d87b88190b038d334f4965369 |
completed | April 14, 2026, 12:12 p.m. |
| PD | Predicate disambiguation | batch_69de05a802ac819090604025aae6a4d5 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 9, 2026, 10:19 p.m.