Triple
T3710513
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oskar Schindler |
E81398
|
entity |
| Predicate | usedMeansToRescue |
P50948
|
FINISHED |
| Object | employment in war-related factories |
—
|
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: employment in war-related factories | Statement: [Oskar Schindler, usedMeansToRescue, employment in war-related factories]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedMeansToRescue Context triple: [Oskar Schindler, usedMeansToRescue, employment in war-related factories]
-
A.
allegedRescueEvent
Indicates that an event is claimed or reported to be a rescue, but its occurrence or nature is not confirmed as factual.
-
B.
rescuedBy
Indicates that one entity has been saved or brought out of danger by another entity.
-
C.
helpedEscape
Indicates that one entity assisted another in getting away from confinement, danger, or pursuit.
-
D.
rescuesContext
Indicates that one entity saves or delivers another entity from danger, harm, or a problematic situation within a specific contextual setting.
-
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. chosen
Provenance (4 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_69ad8b1a81588190b3f27a5483bb610e |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adc584b86c8190ba1a1073da440b07 |
completed | March 8, 2026, 6:52 p.m. |
| PD | Predicate disambiguation | batch_69adc041a8608190a2d543dab6d2ef6c |
completed | March 8, 2026, 6:30 p.m. |
| PDg | Predicate description generation | batch_69adc133ef50819094c2b971f31f1615 |
completed | March 8, 2026, 6:34 p.m. |
Created at: March 8, 2026, 3:33 p.m.