Triple
T511061
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | V-2 rocket |
E10608
|
entity |
| Predicate | killedApproximately |
P1785
|
FINISHED |
| Object | around 9,000 civilians and military personnel |
—
|
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: around 9,000 civilians and military personnel | Statement: [V-2 rocket, killedApproximately, around 9,000 civilians and military personnel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: killedApproximately Context triple: [V-2 rocket, killedApproximately, around 9,000 civilians and military personnel]
-
A.
killedDuring
Indicates that one entity caused the death of another entity in the course of, or as part of, a specified event or time period.
-
B.
killedBy
Indicates that one entity caused the death of another entity.
-
C.
usedMethodOfKilling
Indicates that one entity employed a particular method or means to carry out a killing of another entity.
-
D.
deathToll
chosen
Indicates the number of deaths resulting from a particular event, situation, or cause.
-
E.
estimatedNumberOfPeopleSaved
Indicates the approximate count of individuals whose lives were preserved or harm was averted as a result of a particular action, intervention, or entity.
- 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_69a2e84a0d08819087e01863fcd9abf1 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f165b91c81908c2d2ba15c64b956 |
completed | Feb. 28, 2026, 1:45 p.m. |
| PD | Predicate disambiguation | batch_69a2edfe236481909901cc7d4281b33c |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.