Triple
T1033492
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ted Sarandos |
E22304
|
entity |
| Predicate | startedCareerIn |
P19243
|
FINISHED |
| Object | video rental industry |
—
|
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: video rental industry | Statement: [Ted Sarandos, startedCareerIn, video rental industry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: startedCareerIn Context triple: [Ted Sarandos, startedCareerIn, video rental industry]
-
A.
launchedCareerOf
Indicates that one entity’s actions, support, or involvement initiated or significantly advanced another entity’s professional career.
-
B.
careerStart
Indicates the point in time when an entity begins its professional career or main occupational activity.
-
C.
occupationBegan
Indicates the point in time when an entity started holding a particular occupation or job.
-
D.
partOfCareer
chosen
Indicates that one entity represents a role, position, or period that forms a component or phase within another entity’s overall career.
-
E.
hasCareerTrack
Indicates that an entity is associated with or follows a particular career path or professional progression.
- 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_69a493d848848190aed4011b34b2e8d3 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b8d669448190955507e2e4975b9f |
completed | March 1, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69a4b728ad3481909cf1430349cb9bba |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:41 p.m.