Triple
T31902948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Operation Rajiv |
E814471
|
entity |
| Predicate | opponentCasualties |
P52637
|
FINISHED |
| Object | Pakistani Army personnel killed and wounded |
—
|
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: Pakistani Army personnel killed and wounded | Statement: [Operation Rajiv, opponentCasualties, Pakistani Army personnel killed and wounded]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: opponentCasualties Context triple: [Operation Rajiv, opponentCasualties, Pakistani Army personnel killed and wounded]
-
A.
nativeCasualties
Indicates that native or indigenous people suffered deaths or injuries as a result of a particular event, action, or conflict.
-
B.
casualties
Indicates that an event, action, or situation resulted in people being killed or injured.
-
C.
casualtiesInflictedOn
chosen
Indicates that one party has caused deaths or injuries to another party as a result of a harmful event or action.
-
D.
militaryCasualtiesSide
Indicates the side or party in a conflict to which the recorded military casualties belong.
-
E.
sustainedHeavyCasualtiesAt
Indicates that an entity experienced a large number of serious losses (e.g., deaths or injuries) at a specific location or during a specific event.
- 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_69f348f04d7881909537fc9e7cbc670e |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6c1bb5f248190834161b5a6ba1ece |
completed | May 3, 2026, 3:32 a.m. |
| PD | Predicate disambiguation | batch_69f6bd25bed08190befcabd3a41ffadf |
completed | May 3, 2026, 3:12 a.m. |
Created at: May 1, 2026, midnight