Triple
T35310101
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Höfle Telegram |
E1019745
|
entity |
| Predicate | listsVictimNumbersFor |
P125138
|
FINISHED |
| Object | Belzec extermination camp |
—
|
NE NERFINISHED |
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: Belzec extermination camp | Statement: [Höfle Telegram, listsVictimNumbersFor, Belzec extermination camp]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: listsVictimNumbersFor Context triple: [Höfle Telegram, listsVictimNumbersFor, Belzec extermination camp]
-
A.
victimsOfCrime
Indicates that the subject has suffered harm or loss as a result of a criminal act committed by the object.
-
B.
hasVictimsCount
chosen
Indicates the number of victims associated with a particular event, action, or entity.
-
C.
estimatedVictimsUnderAuthority
Indicates that a specified authority is estimated to have a certain number of victims under its control, influence, or jurisdiction.
-
D.
numberOfVictimsClaimed
Indicates the reported count of victims associated with a particular event, incident, or action.
-
E.
numberOfChildVictims
Indicates the count of individuals who are victims and are classified as children in the context of the described event or situation.
- 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_69f76de8b4c48190ae504b86185c474c |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f79da9f80c8190b0afd8509f28747b |
completed | May 3, 2026, 7:10 p.m. |
| PD | Predicate disambiguation | batch_69f79617d40481909ba372f94209c08b |
completed | May 3, 2026, 6:38 p.m. |
Created at: May 3, 2026, 4:03 p.m.