Triple
T17860736
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shimbun Akahata |
E446063
|
entity |
| Predicate | stanceOnMilitarism |
P129039
|
FINISHED |
| Object | opposed |
—
|
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: opposed | Statement: [Shimbun Akahata, stanceOnMilitarism, opposed]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: stanceOnMilitarism Context triple: [Shimbun Akahata, stanceOnMilitarism, opposed]
-
A.
stanceOnNationalism
Indicates a subject’s attitude, position, or viewpoint regarding nationalism as an ideology or political principle.
-
B.
opposedWar
Indicates that an entity actively resisted, disagreed with, or worked against a particular war or military conflict.
-
C.
stanceTowardGovernment
Indicates the attitude, position, or level of support or opposition that an entity holds toward a government.
-
D.
isMilitarized
Indicates that an entity is organized, equipped, or structured according to military principles, forces, or purposes.
-
E.
wasMilitarized
Indicates that an entity underwent a process of being organized, equipped, or adapted for military use or purposes.
- F. None of above. chosen
Provenance (4 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_69d8b9f26f18819089c9e43250bee6ae |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4979013dc8190beb7718f0ba92bc3 |
completed | April 19, 2026, 8:51 a.m. |
| PD | Predicate disambiguation | batch_69e3d8e6d2e88190ad9ef9f8a99f13e6 |
completed | April 18, 2026, 7:17 p.m. |
| PDg | Predicate description generation | batch_69e3db7704588190a34a422421152173 |
completed | April 18, 2026, 7:28 p.m. |
Created at: April 10, 2026, 10:17 a.m.