Triple
T28638769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Street Kings |
E724861
|
entity |
| Predicate | KeanuReevesRoleType |
P16411
|
FINISHED |
| Object | LAPD detective |
—
|
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: LAPD detective | Statement: [Street Kings, KeanuReevesRoleType, LAPD detective]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: KeanuReevesRoleType Context triple: [Street Kings, KeanuReevesRoleType, LAPD detective]
-
A.
actingRoleType
chosen
Indicates the specific type or category of role an entity performs when acting in a particular capacity or function.
-
B.
creditedRoleOf
Indicates that a particular role or position is formally acknowledged as being held or performed by a specific entity in a credit or attribution context.
-
C.
hasPortrayedPersonRole
Indicates that an entity has performed or held a specific role in portraying a particular person (e.g., in a film, play, or other representation).
-
D.
knownForRoleIn
Indicates that an entity is recognized or notable for performing a particular role in a specific work, project, or context.
-
E.
worksForCharacterPlayedBy
Indicates that one character is employed by, or works under, another character who is portrayed by a specific actor.
- 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_69f01d8328c48190bc0e5f9b9b848582 |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69f652a84f848190b5898ee7566fb84e |
completed | May 2, 2026, 7:38 p.m. |
| PD | Predicate disambiguation | batch_69f651aad92c8190b874b3b5f9f64434 |
completed | May 2, 2026, 7:34 p.m. |
Created at: April 28, 2026, 4:42 a.m.