Triple
T37135997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Cosa Nostra |
E919964
|
entity |
| Predicate | typicalCriminalActivity |
P7957
|
FINISHED |
| Object | extortion |
—
|
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: extortion | Statement: [La Cosa Nostra, typicalCriminalActivity, extortion]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalCriminalActivity Context triple: [La Cosa Nostra, typicalCriminalActivity, extortion]
-
A.
scaleOfCriminalActivity
Indicates the relative extent or magnitude of involvement in criminal activity associated with an entity or event.
-
B.
criminalType
Indicates the specific category or classification of crime associated with a criminal act or offender.
-
C.
crimeType
chosen
Indicates the specific category or nature of the crime associated with an event or entity.
-
D.
committedCrime
Indicates that an entity has carried out or been responsible for a criminal act or offense.
-
E.
crimeAdditionalAct
Indicates that an additional criminal act is associated with, or occurs in conjunction with, a primary crime.
- 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_69f76e9e9d008190a250b0387c992c74 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a037c8e2c648190a65fc9c7872861af |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a11efc08190bb7cacc1325b4dc6 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:15 p.m.