Triple
T22224088
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jesse James Hollywood |
E549288
|
entity |
| Predicate | criminalChargeRelatedTo |
P77292
|
FINISHED |
| Object | marijuana trafficking |
—
|
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: marijuana trafficking | Statement: [Jesse James Hollywood, criminalChargeRelatedTo, marijuana trafficking]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: criminalChargeRelatedTo Context triple: [Jesse James Hollywood, criminalChargeRelatedTo, marijuana trafficking]
-
A.
crimeCharged
chosen
Indicates that legal authorities have formally accused an entity of committing a specific crime.
-
B.
crimeAccusation
Indicates that one entity formally accuses another entity of having committed a crime.
-
C.
crimeType
Indicates the specific category or nature of the crime associated with an event or entity.
-
D.
criminalType
Indicates the specific category or classification of crime associated with a criminal act or offender.
-
E.
committedCrime
Indicates that an entity has carried out or been responsible for a criminal act 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_69e11e403d6481909a94d0aaf157f6ef |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12b92b79c8190a8a4d33591abd763 |
completed | April 28, 2026, 9:50 p.m. |
| PD | Predicate disambiguation | batch_69e71b4dcc408190a30429fb08fcf39e |
completed | April 21, 2026, 6:38 a.m. |
Created at: April 16, 2026, 8:37 p.m.