Triple
T14352982
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Huon Peninsula campaign |
E355899
|
entity |
| Predicate | involvedTypeOfWarfare |
P1403
|
FINISHED |
| Object | amphibious warfare |
—
|
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: amphibious warfare | Statement: [Huon Peninsula campaign, involvedTypeOfWarfare, amphibious warfare]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: involvedTypeOfWarfare Context triple: [Huon Peninsula campaign, involvedTypeOfWarfare, amphibious warfare]
-
A.
warfareType
chosen
Indicates the specific kind or category of warfare that characterizes a given conflict or military engagement.
-
B.
opposedWarfareType
Indicates that one entity is in opposition to, or acts against, the type or form of warfare represented by the other entity.
-
C.
usedWarfareType
Indicates the specific type or method of warfare that an entity employed in a conflict or military context.
-
D.
levelOfWar
Indicates the specific intensity or scale of military conflict at which an operation, action, or relationship is occurring (e.g., tactical, operational, strategic).
-
E.
militaryConflict
Indicates a relationship where two or more parties are engaged in organized, armed hostilities or warfare against each other.
- 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_69d82790a7e08190877e2d349b2e8d8e |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8f4ff1e48190bd9419d70098cede |
completed | April 14, 2026, 7:02 p.m. |
| PD | Predicate disambiguation | batch_69de2a9958e881909d03ac03f135163e |
completed | April 14, 2026, 11:52 a.m. |
Created at: April 10, 2026, 1:14 a.m.