Triple
T19412185
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bufalino crime family |
E485612
|
entity |
| Predicate | criminalSpecialization |
P135782
|
FINISHED |
| Object | control of trucking and freight businesses |
—
|
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: control of trucking and freight businesses | Statement: [Bufalino crime family, criminalSpecialization, control of trucking and freight businesses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: criminalSpecialization Context triple: [Bufalino crime family, criminalSpecialization, control of trucking and freight businesses]
-
A.
criminalType
Indicates the specific category or classification of crime associated with a criminal act or offender.
-
B.
crimeType
Indicates the specific category or nature of the crime associated with an event or entity.
-
C.
detectiveType
Indicates that one entity is classified as a particular type or category of detective in relation to another entity.
-
D.
recognitionOfCrimes
Indicates the formal acknowledgment or identification that certain actions or events constitute crimes under a legal or normative framework.
-
E.
committedCrime
Indicates that an entity has carried out or been responsible for a criminal act or offense.
- 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_69d8e8d5162481909db12435d9535c1a |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e62af681288190ba2ec52d5adb6a22 |
completed | April 20, 2026, 1:32 p.m. |
| PD | Predicate disambiguation | batch_69e4fd68b1f881908d273de1fee81a75 |
completed | April 19, 2026, 4:06 p.m. |
| PDg | Predicate description generation | batch_69e5004c23308190a087b7941a90725f |
completed | April 19, 2026, 4:18 p.m. |
Created at: April 10, 2026, 1:37 p.m.