Triple
T5067547
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gaten Matarazzo |
E114179
|
entity |
| Predicate | Prank Encounters |
P61064
|
FINISHED |
| Object | distributedBy Netflix |
—
|
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: distributedBy Netflix | Statement: [Gaten Matarazzo, Prank Encounters, distributedBy Netflix]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: Prank Encounters Context triple: [Gaten Matarazzo, Prank Encounters, distributedBy Netflix]
-
A.
tricked
Indicates that one entity intentionally deceived another into believing something false or acting under a false impression.
-
B.
shootsCatches
Indicates that one entity shoots something that is then caught by another entity.
-
C.
runsInto
Indicates that one entity moves so as to collide or come into sudden contact with another entity.
-
D.
trapType
Indicates the specific kind or category of trap associated with an entity or situation.
-
E.
catches
Indicates that one entity successfully seizes, intercepts, or takes hold of another entity, often stopping its motion or preventing its escape.
- 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_69bd443cf28c8190ad371d603563dbdd |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd749bf69c819093e75dce56f1c0ab |
completed | March 20, 2026, 4:23 p.m. |
| PD | Predicate disambiguation | batch_69bd715622b48190a3e8e49a5ef62b4a |
completed | March 20, 2026, 4:09 p.m. |
| PDg | Predicate description generation | batch_69bd738ac2e0819099c06cdcc5e21d28 |
completed | March 20, 2026, 4:19 p.m. |
Created at: March 20, 2026, 1:38 p.m.