Triple
T36936
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Warsaw Uprising |
E731
|
entity |
| Predicate | civilianCasualties |
P1785
|
FINISHED |
| Object | approximately 150,000–200,000 killed |
—
|
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: approximately 150,000–200,000 killed | Statement: [Warsaw Uprising, civilianCasualties, approximately 150,000–200,000 killed]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: civilianCasualties Context triple: [Warsaw Uprising, civilianCasualties, approximately 150,000–200,000 killed]
-
A.
casualties
Indicates that an event, action, or situation resulted in people being killed or injured.
-
B.
civilianImpact
Indicates the extent to which an action, event, or situation affects civilians, especially in terms of harm, disruption, or other consequences.
-
C.
casualtiesEstimate
Indicates an estimated number of people killed, injured, or otherwise harmed as a result of an event or incident.
-
D.
civilianDisplacement
Indicates the forced or compelled movement of civilian populations from their homes or usual places of residence, typically due to conflict, violence, or persecution.
-
E.
deathToll
chosen
Indicates the number of deaths resulting from a particular event, situation, or cause.
- 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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24bb753f081909cd8b25cfb8e08af |
completed | Feb. 28, 2026, 1:58 a.m. |
| PD | Predicate disambiguation | batch_69a24ab4a6908190b6f355415ffe7948 |
completed | Feb. 28, 2026, 1:53 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.