Triple
T14891270
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | iStockphoto |
E359757
|
entity |
| Predicate | contributorCompensationModel |
P58774
|
FINISHED |
| Object | royalty payments |
—
|
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: royalty payments | Statement: [iStockphoto, contributorCompensationModel, royalty payments]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: contributorCompensationModel Context triple: [iStockphoto, contributorCompensationModel, royalty payments]
-
A.
compensationModel
chosen
Indicates the type or structure of payment or rewards provided in exchange for work, services, or performance.
-
B.
memberRemuneration
Indicates that a member receives payment or compensation, typically for their role, services, or participation within an organization or group.
-
C.
rewardModel
Indicates a relationship where one entity serves as a model or framework for assigning rewards or evaluating outcomes for another entity or process.
-
D.
compensationCategory
Indicates the type or classification of compensation associated with an entity, such as how or in what form payment or remuneration is provided.
-
E.
relatedCommission
Indicates that one commission is connected or associated with another commission in a relevant contextual or functional way.
- 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_69d827980cbc8190a0c569ae3940a1d9 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69ded5f883288190af602633fa7d6860 |
completed | April 15, 2026, 12:04 a.m. |
| PD | Predicate disambiguation | batch_69de8c1a2bcc81908f914e2e2ced65eb |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 2:10 a.m.