Triple
T13796482
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Django (1966 film) |
E331529
|
entity |
| Predicate | violenceLedTo |
P111493
|
FINISHED |
| Object | censorship issues in several countries |
—
|
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: censorship issues in several countries | Statement: [Django (1966 film), violenceLedTo, censorship issues in several countries]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: violenceLedTo Context triple: [Django (1966 film), violenceLedTo, censorship issues in several countries]
-
A.
justifiesViolenceThrough
Indicates that one party legitimizes or defends the use of violence by appealing to, or reasoning through, another factor, belief, or circumstance.
-
B.
battleLedTo
Indicates that one battle resulted in, caused, or directly brought about another event, state, or outcome.
-
C.
violenceLevel
Indicates the degree or intensity of violent behavior, actions, or content present in or associated with an entity.
-
D.
timeOfMassViolence
Indicates the specific time at which an act or event of mass violence occurred.
-
E.
containsViolence
Indicates that the subject includes, depicts, or involves acts of physical harm, aggression, or violent behavior.
- 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_69d81c58feb08190a77bca8bf7d6d20f |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de025be1f08190aac525d72d7dc0c3 |
completed | April 14, 2026, 9:01 a.m. |
| PD | Predicate disambiguation | batch_69dbc85fb600819098a2aab48169be96 |
completed | April 12, 2026, 4:29 p.m. |
| PDg | Predicate description generation | batch_69dcad0eea9881908f71e1eed9a2446b |
completed | April 13, 2026, 8:45 a.m. |
Created at: April 9, 2026, 10:11 p.m.