Triple
T29843774
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | David Brian as Dan Reynolds |
E757872
|
entity |
| Predicate | hasProfessionOfActor |
P153983
|
FINISHED |
| Object | David Brian |
—
|
NE NERFINISHED |
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: David Brian | Statement: [David Brian as Dan Reynolds, hasProfessionOfActor, David Brian]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProfessionOfActor Context triple: [David Brian as Dan Reynolds, hasProfessionOfActor, David Brian]
-
A.
actorNotableOccupation
Indicates that a person (typically an actor) is associated with a particular occupation or professional role for which they are especially well known.
-
B.
leadActorOccupation
Indicates that the occupation specified is the primary professional role of the lead actor in a given work or context.
-
C.
mainProfessionOfDirector
Indicates the primary professional occupation or field in which a given director mainly works.
-
D.
portrayedProfessionOfCharacter
chosen
Indicates that one entity is the profession or occupation depicted as being held by a particular character.
-
E.
hasFilmographyType
Indicates the type or category of film-related work associated with an entity (e.g., actor, director, producer) within its filmography.
- 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_69f224593f6c81908785a560fe659f58 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69ffa9677be08190852c8ef6c2545fed |
completed | May 9, 2026, 9:38 p.m. |
| PD | Predicate disambiguation | batch_69ffa6570e2c8190a9d7b37f12b91d9a |
completed | May 9, 2026, 9:25 p.m. |
Created at: April 29, 2026, 5:40 p.m.