Triple
T16816659
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Dubienka |
E408768
|
entity |
| Predicate | casualtiesPolish |
P23378
|
FINISHED |
| Object | several hundred 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: several hundred killed and wounded | Statement: [Battle of Dubienka, casualtiesPolish, several hundred killed and wounded]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: casualtiesPolish Context triple: [Battle of Dubienka, casualtiesPolish, several hundred killed and wounded]
-
A.
PolishCasualties
chosen
Indicates the number or extent of casualties suffered by Polish forces or population in a given conflict or event.
-
B.
nativeCasualties
Indicates that native or indigenous people suffered deaths or injuries as a result of a particular event, action, or conflict.
-
C.
casualties
Indicates that an event, action, or situation resulted in people being killed or injured.
-
D.
primaryCasualtiesFrom
Indicates that an entity is the main source or cause of the casualties experienced by another entity.
-
E.
militaryCasualtiesSide
Indicates the side or party in a conflict to which the recorded military casualties belong.
- 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_69d88394566c8190b3dcbdc72935f7fa |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b2e1de908190aa3508770fb865cf |
completed | April 18, 2026, 4:35 p.m. |
| PD | Predicate disambiguation | batch_69e32b814b188190aee525f8779203cd |
completed | April 18, 2026, 6:58 a.m. |
Created at: April 10, 2026, 5:23 a.m.