Triple
T28211682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brent Maverick |
E711184
|
entity |
| Predicate | usesViolenceSparingly |
P80416
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Brent Maverick, usesViolenceSparingly, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesViolenceSparingly Context triple: [Brent Maverick, usesViolenceSparingly, yes]
-
A.
containsViolence
Indicates that the subject includes, depicts, or involves acts of physical harm, aggression, or violent behavior.
-
B.
containsGraphicViolence
Indicates that the subject includes depictions of explicit, intense, or realistic physical harm or brutality.
-
C.
usedViolenceFor
Indicates that an entity employed physical force or violent means in order to achieve, enable, or support a particular goal, outcome, or activity.
-
D.
justifiesViolenceThrough
Indicates that one party legitimizes or defends the use of violence by appealing to, or reasoning through, another factor, belief, or circumstance.
-
E.
violenceLevel
chosen
Indicates the degree or intensity of violent behavior, actions, or content present in or associated with an entity.
- 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_69efb51cb5288190818c1f63a266af11 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69f65876c52c8190bc889c7a67bd07f3 |
completed | May 2, 2026, 8:03 p.m. |
| PD | Predicate disambiguation | batch_69f6575d89788190aca478e4aea05a65 |
completed | May 2, 2026, 7:58 p.m. |
Created at: April 27, 2026, 10:39 p.m.