Triple
T511062
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | V-2 rocket |
E10608
|
entity |
| Predicate | causedForcedLaborDeaths |
P700
|
FINISHED |
| Object | around 12,000 concentration camp prisoners |
—
|
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 12,000 concentration camp prisoners | Statement: [V-2 rocket, causedForcedLaborDeaths, around 12,000 concentration camp prisoners]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: causedForcedLaborDeaths Context triple: [V-2 rocket, causedForcedLaborDeaths, around 12,000 concentration camp prisoners]
-
A.
numberOfPeoplePressedToDeath
Indicates the number of people who were killed specifically by being pressed to death.
-
B.
deathToll
Indicates the number of deaths resulting from a particular event, situation, or cause.
-
C.
deathTollEstimate
chosen
Indicates an estimated number of deaths attributed to a particular event, cause, or period.
-
D.
causeOfDeath
Indicates the specific factor, event, or condition that directly resulted in an entity’s death.
-
E.
notableVictim
Indicates that the subject is a person or entity who is notably recognized as a victim of the object (such as an event, crime, or harmful action).
- 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.