Triple
T6537479
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Franz von Papen |
E168199
|
entity |
| Predicate | subsequentConviction |
P6201
|
FINISHED |
| Object | denazification court sentenced him to prison |
—
|
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: denazification court sentenced him to prison | Statement: [Franz von Papen, subsequentConviction, denazification court sentenced him to prison]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subsequentConviction Context triple: [Franz von Papen, subsequentConviction, denazification court sentenced him to prison]
-
A.
convictedOf
chosen
Indicates that a person or entity has been found guilty of committing a specified offense or crime through a formal legal process.
-
B.
numberOfConvictions
Indicates the count of times an entity has been formally found guilty of an offense.
-
C.
hasFirstConviction
Indicates that an entity has received its first legal conviction for an offense.
-
D.
isForConvictedOffenders
Indicates that something is intended to apply to, be used by, or be relevant for individuals who have been legally convicted of offenses.
-
E.
convictedBy
Indicates that an authority, typically a court or judge, has formally found an entity guilty of a crime or offense.
- 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_69c68a51564081909e93aee0dbd9cca3 |
completed | March 27, 2026, 1:46 p.m. |
| NER | Named-entity recognition | batch_69c6ce07332481909a5a7964282eb776 |
completed | March 27, 2026, 6:35 p.m. |
| PD | Predicate disambiguation | batch_69c6acf3e3708190b052ec774e607cb7 |
completed | March 27, 2026, 4:14 p.m. |
Created at: March 27, 2026, 1:49 p.m.