Triple
T32637511
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dewey Crowe |
E834386
|
entity |
| Predicate | scaleOfCriminalActivity |
P174755
|
FINISHED |
| Object | small-time criminal |
—
|
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: small-time criminal | Statement: [Dewey Crowe, scaleOfCriminalActivity, small-time criminal]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: scaleOfCriminalActivity Context triple: [Dewey Crowe, scaleOfCriminalActivity, small-time criminal]
-
A.
criminalType
Indicates the specific category or classification of crime associated with a criminal act or offender.
-
B.
crimeRate
Indicates the frequency or level of criminal activity occurring within a given area or population.
-
C.
regionOfCrimes
Indicates the geographic area or jurisdiction in which the crimes occurred or are attributed to an entity.
-
D.
crimeType
Indicates the specific category or nature of the crime associated with an event or entity.
-
E.
country of criminal activity
Indicates the country in which the criminal activity took place or was primarily carried out.
- 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_69f3492dc2308190a88c6e30a3f3f576 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6c7e32ec08190b74856937c4a9fc3 |
completed | May 3, 2026, 3:58 a.m. |
| PD | Predicate disambiguation | batch_69f6c3f617c08190a70ba880210f908c |
completed | May 3, 2026, 3:41 a.m. |
| PDg | Predicate description generation | batch_69f6c77500a08190b2bdeca33bd2ac08 |
completed | May 3, 2026, 3:56 a.m. |
Created at: May 1, 2026, 1:07 a.m.