Triple
T6084726
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mona Rudao |
E135608
|
entity |
| Predicate | aftermathOfActions |
P48782
|
FINISHED |
| Object | severe Japanese military retaliation |
—
|
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: severe Japanese military retaliation | Statement: [Mona Rudao, aftermathOfActions, severe Japanese military retaliation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: aftermathOfActions Context triple: [Mona Rudao, aftermathOfActions, severe Japanese military retaliation]
-
A.
aftermathOf
chosen
Indicates that one event, state, or condition occurs as a consequence or result following another event.
-
B.
aftermathPolicy
Indicates the policy, rules, or procedures that apply in the aftermath or consequences of a particular event or action.
-
C.
handledAftermathOf
Indicates that one entity took responsibility for dealing with the consequences or fallout that occurred after another entity’s actions or events.
-
D.
after
Indicates that one event, state, or action occurs later in time than another, following it in temporal order.
-
E.
impactOutcome
Indicates that one entity produces an effect or influence that changes the result, consequence, or final state of another entity or situation.
- 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_69c0087bcc788190b20f093d3a6c60ec |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c057891dc88190997c4e32b2261fb3 |
completed | March 22, 2026, 8:56 p.m. |
| PD | Predicate disambiguation | batch_69c049f3b1ec8190bea67a7bec6442a5 |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:12 p.m.