Triple
T165445
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Netflix, Inc. |
E3004
|
entity |
| Predicate | formerBusinessModel |
P5635
|
FINISHED |
| Object | DVD-by-mail rental |
—
|
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: DVD-by-mail rental | Statement: [Netflix, Inc., formerBusinessModel, DVD-by-mail rental]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: formerBusinessModel Context triple: [Netflix, Inc., formerBusinessModel, DVD-by-mail rental]
-
A.
formerBrand
Indicates that an entity was previously used or recognized as a brand for another entity but is no longer its current brand.
-
B.
formerName
Indicates that an entity was previously known by a different name in the past.
-
C.
formerEmployer
Indicates that one entity previously employed the other but no longer does so.
-
D.
formerPublisher
Indicates that one entity previously served as the publisher of another entity but no longer holds that role.
-
E.
formerCurrency
Indicates that an entity was once used as a currency in a place or time period but is no longer in official use.
- 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_69a2524ce1e48190ab066bf72859f474 |
completed | Feb. 28, 2026, 2:26 a.m. |
| NER | Named-entity recognition | batch_69a25883ac8481909616b2179561bd98 |
completed | Feb. 28, 2026, 2:52 a.m. |
| PD | Predicate disambiguation | batch_69a25664ba8081908ac298511a9fc5ba |
completed | Feb. 28, 2026, 2:43 a.m. |
| PDg | Predicate description generation | batch_69a256eb46ec81909c730000e5041d0d |
completed | Feb. 28, 2026, 2:46 a.m. |
Created at: Feb. 28, 2026, 2:34 a.m.