Triple
T3688526
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leipzig-Thekla subcamp |
E78283
|
entity |
| Predicate | hasPrisonerCondition |
P50327
|
FINISHED |
| Object | brutal living conditions |
—
|
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: brutal living conditions | Statement: [Leipzig-Thekla subcamp, hasPrisonerCondition, brutal living conditions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPrisonerCondition Context triple: [Leipzig-Thekla subcamp, hasPrisonerCondition, brutal living conditions]
-
A.
hasPrison
Indicates that one entity possesses, contains, or is the location of a prison associated with another entity.
-
B.
manyPrisonersCondition
Indicates a situation in which a large number of individuals are held in prison or detention, emphasizing the condition of having many prisoners.
-
C.
hasPrisonerCategory
Indicates the classification or category assigned to a prisoner within a correctional or detention system.
-
D.
hasJail
Indicates that one entity possesses, operates, or is associated with a jail or detention facility.
-
E.
hasBeenImprisonedBy
Indicates that one entity has been confined or incarcerated under the authority or control of another 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_69ad85e285a081908f8cbfa9e2ed9b75 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc4c960788190b73ede08658846aa |
completed | March 8, 2026, 6:49 p.m. |
| PD | Predicate disambiguation | batch_69adb84be1fc81909721c871babb4633 |
completed | March 8, 2026, 5:56 p.m. |
| PDg | Predicate description generation | batch_69adb97cdb788190a5ce96b21bd157ab |
completed | March 8, 2026, 6:01 p.m. |
Created at: March 8, 2026, 3:26 p.m.