Triple
T5037129
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 永野修身 |
E113452
|
entity |
| Predicate | 関与した戦争・紛争 |
P13112
|
FINISHED |
| Object | 日露戦争 |
—
|
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: 日露戦争 | Statement: [永野修身, 関与した戦争・紛争, 日露戦争]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 関与した戦争・紛争 Context triple: [永野修身, 関与した戦争・紛争, 日露戦争]
-
A.
militaryConflict
Indicates a relationship where two or more parties are engaged in organized, armed hostilities or warfare against each other.
-
B.
warParticipatedIn
chosen
Indicates that an entity took part as a combatant or active participant in a specific war or armed conflict.
-
C.
associatedWithWar
Indicates a relationship where an entity is connected or related to war, such as by involvement, influence, cause, or context.
-
D.
militaryConflictCovered
Indicates that one entity (such as a document, report, or media item) covers, describes, or reports on a specific military conflict involving another entity.
-
E.
hasPartOfConflict
Indicates that one conflict includes another conflict as a constituent or subordinate part of it.
- 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_69bd44384298819089c49e7c330ec7b8 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd73bb069c8190af86f1b2f95f3d95 |
completed | March 20, 2026, 4:20 p.m. |
| PD | Predicate disambiguation | batch_69bd71529d608190a53470ba6c14bb1d |
completed | March 20, 2026, 4:09 p.m. |
Created at: March 20, 2026, 1:37 p.m.