Triple
T26101950
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yoshio Yoda |
E658429
|
entity |
| Predicate | portrayedMilitaryRole |
P170341
|
FINISHED |
| Object | Japanese prisoner of war |
—
|
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: Japanese prisoner of war | Statement: [Yoshio Yoda, portrayedMilitaryRole, Japanese prisoner of war]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portrayedMilitaryRole Context triple: [Yoshio Yoda, portrayedMilitaryRole, Japanese prisoner of war]
-
A.
militaryRole
Indicates the specific function, position, or duty an entity holds within a military organization or context.
-
B.
favoredMilitaryRole
Indicates the military position, function, or specialization that an entity prefers or is most inclined to choose.
-
C.
formerMilitaryRole
Indicates that an entity previously held, but no longer holds, a specific military role or position.
-
D.
portraysMilitaryUnit
Indicates that one entity visually or narratively represents, depicts, or shows a specific military unit.
-
E.
servedInRole
Indicates that one entity performed duties or held a position within a specified role or office in relation to another entity.
- 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_69ee5bc09c288190bc42a11972841383 |
completed | April 26, 2026, 6:38 p.m. |
| NER | Named-entity recognition | batch_69f69063edbc81909e7735954aabee0b |
completed | May 3, 2026, 12:01 a.m. |
| PD | Predicate disambiguation | batch_69f68b78f29481908cc8f390496dee97 |
completed | May 2, 2026, 11:40 p.m. |
| PDg | Predicate description generation | batch_69f68f6584a88190a8c4d95c0c84bee9 |
completed | May 2, 2026, 11:57 p.m. |
Created at: April 26, 2026, 7:55 p.m.