Triple
T16008417
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joseph Kosinski |
E388275
|
entity |
| Predicate | hasDirectedBlockbusters |
P120749
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Joseph Kosinski, hasDirectedBlockbusters, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDirectedBlockbusters Context triple: [Joseph Kosinski, hasDirectedBlockbusters, true]
-
A.
workedAsDirectorFor
Indicates that one entity held the role of director in relation to another entity, such as an organization, project, or production.
-
B.
hasDirectorStar
Indicates that a person both directed and starred in the same work (e.g., film, show, or production).
-
C.
hasDirectedActor
Indicates that an entity has served as the director of another entity, such as a film, play, or production.
-
D.
hasDirectorInKeyWork
Indicates that a director is associated with a key or primary work of an entity (such as a film, series, or major production).
-
E.
numberOfFilmsDirected
Indicates the total count of films that a given entity has directed.
- 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_69d86dabcb7c8190b6a39d6831d2fa1b |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e173b3bf6c81909230170e833d7ce7 |
completed | April 16, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69e142dc081c819082527e3fa8773460 |
completed | April 16, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69e173af801c8190bfc0f602831bb594 |
completed | April 16, 2026, 11:41 p.m. |
Created at: April 10, 2026, 4:55 a.m.