Triple
T12914109
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vergeltungswaffe 2 |
E308931
|
entity |
| Predicate | militaryCasualtiesCaused |
P6773
|
FINISHED |
| Object | several thousand |
—
|
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 thousand | Statement: [Vergeltungswaffe 2, militaryCasualtiesCaused, several thousand]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: militaryCasualtiesCaused Context triple: [Vergeltungswaffe 2, militaryCasualtiesCaused, several thousand]
-
A.
nativeCasualties
Indicates that native or indigenous people suffered deaths or injuries as a result of a particular event, action, or conflict.
-
B.
militaryCasualtiesEstimate
chosen
Indicates an estimated number of people killed, wounded, or missing as a result of military conflict or operations.
-
C.
governmentCasualties
Indicates that members of a government (such as officials, employees, or security forces) were killed, injured, or otherwise became casualties in an event or conflict.
-
D.
militaryCasualtiesSide
Indicates the side or party in a conflict to which the recorded military casualties belong.
-
E.
casualtiesCountry
Indicates that the specified country is the one in which the recorded casualties (deaths or injuries) occurred or to which those 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_69d7bdf92b588190acdf2a2291ac4590 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d971a0d6508190bca9668e9e06abfe |
completed | April 10, 2026, 9:54 p.m. |
| PD | Predicate disambiguation | batch_69d96fa9b7708190a9e9fa30f59ff580 |
completed | April 10, 2026, 9:46 p.m. |
Created at: April 9, 2026, 5:41 p.m.