Triple
T805788
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Periscope |
E17428
|
entity |
| Predicate | hasMonetizationModel |
P59
|
FINISHED |
| Object | free to use |
—
|
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: free to use | Statement: [Periscope, hasMonetizationModel, free to use]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMonetizationModel Context triple: [Periscope, hasMonetizationModel, free to use]
-
A.
supportsMonetizationProgram
Indicates that an entity enables or is compatible with a specific monetization program, allowing revenue-generating activities to occur.
-
B.
fundingModel
chosen
Indicates how an entity is financially supported or sustained, such as through specific revenue sources, payment structures, or funding mechanisms.
-
C.
canBePurchasedWith
Indicates that one entity is able to be bought or acquired using another entity as the form of payment.
-
D.
hasMonetaryGrant
Indicates that an entity provides or receives a monetary grant from another entity.
-
E.
hasTradingModel
Indicates that one entity uses, is governed by, or is associated with a particular trading model.
- 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_69a4937ae8a08190b5084a03d532b30e |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4ace495348190aec66f35ea90bc89 |
completed | March 1, 2026, 9:17 p.m. |
| PD | Predicate disambiguation | batch_69a4aa70973c8190adbf08302d1103a9 |
completed | March 1, 2026, 9:06 p.m. |
Created at: March 1, 2026, 7:38 p.m.