Triple
T33404271
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ben (Burning) |
E855394
|
entity |
| Predicate | impliedCriminalActivity |
P143570
|
FINISHED |
| Object | serial arson of greenhouses |
—
|
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: serial arson of greenhouses | Statement: [Ben (Burning), impliedCriminalActivity, serial arson of greenhouses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: impliedCriminalActivity Context triple: [Ben (Burning), impliedCriminalActivity, serial arson of greenhouses]
-
A.
illegalActivityAssociatedWith
Indicates that there is a connection between an entity and an unlawful or criminal activity.
-
B.
scaleOfCriminalActivity
Indicates the relative extent or magnitude of involvement in criminal activity associated with an entity or event.
-
C.
makesConductIllegal
Indicates that a law or rule renders a particular type of conduct prohibited and subject to legal penalties.
-
D.
hasCriminalElement
chosen
Indicates that the subject involves, contains, or is associated with an illegal or criminal component, activity, or characteristic.
-
E.
isCriminalizedIn
Indicates that a specific behavior, action, or condition is prohibited and subject to legal penalties within a particular jurisdiction or legal 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_69f3496e3f1c8190bcecfa82aa9d17ff |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6f38159d08190980ad639e08f00f4 |
completed | May 3, 2026, 7:04 a.m. |
| PD | Predicate disambiguation | batch_69f6e3d7bee48190b94e0beb48a1d7fa |
completed | May 3, 2026, 5:57 a.m. |
Created at: May 1, 2026, 1:36 a.m.