Triple
T122650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | YouTube |
E2481
|
entity |
| Predicate | monetizationModel |
P59
|
FINISHED |
| Object | advertising-supported |
—
|
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: advertising-supported | Statement: [YouTube, monetizationModel, advertising-supported]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: monetizationModel Context triple: [YouTube, monetizationModel, advertising-supported]
-
A.
fundingModel
chosen
Indicates how an entity is financially supported or sustained, such as through specific revenue sources, payment structures, or funding mechanisms.
-
B.
monetaryValue
Indicates the amount of money associated with an entity, event, or transaction.
-
C.
revenueLevel
Indicates the relative amount or tier of revenue associated with an entity or activity.
-
D.
commercializedIn
Indicates that something has been brought to market or made available for commercial sale or use within a specified place or context.
-
E.
mediaMarket
Indicates a relationship where a media outlet or content provider serves, targets, or operates within a particular geographic or demographic market.
- 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_69a2506c5428819085c28a8884790e29 |
completed | Feb. 28, 2026, 2:18 a.m. |
| NER | Named-entity recognition | batch_69a2573b4e7481909ee09d2899f8a74b |
completed | Feb. 28, 2026, 2:47 a.m. |
| PD | Predicate disambiguation | batch_69a2564928208190966a619680a0d6e2 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:24 a.m.