Triple
T30536433
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Federal Correctional Institution, Otisville |
E777159
|
entity |
| Predicate | typeOfOffenders |
P102118
|
FINISHED |
| Object | white-collar criminals |
—
|
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: white-collar criminals | Statement: [Federal Correctional Institution, Otisville, typeOfOffenders, white-collar criminals]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfOffenders Context triple: [Federal Correctional Institution, Otisville, typeOfOffenders, white-collar criminals]
-
A.
targetOffenderType
Indicates the specific category or type of offender that an action, rule, or condition is directed toward.
-
B.
criminalType
Indicates the specific category or classification of crime associated with a criminal act or offender.
-
C.
perpetratorType
Indicates the classification or category of the entity that carried out or is responsible for a harmful, illegal, or otherwise wrongful act.
-
D.
typeOfConvict
Indicates the specific category or classification of a convict in relation to their conviction or legal status.
-
E.
prisonerType
chosen
Indicates the classification or category assigned to a prisoner within a correctional or detention system.
- 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_69f2249d183c8190b79937c1768d2163 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69ff691f5ae481908597ce245188d31c |
completed | May 9, 2026, 5:04 p.m. |
| PD | Predicate disambiguation | batch_69ff67ceeeb081909fd00cad166c4b6a |
completed | May 9, 2026, 4:58 p.m. |
Created at: April 29, 2026, 8:18 p.m.