Triple
T4672306
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Herbalife |
E103591
|
entity |
| Predicate | compensationModel |
P58774
|
FINISHED |
| Object | multi-level distributor commissions |
—
|
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: multi-level distributor commissions | Statement: [Herbalife, compensationModel, multi-level distributor commissions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: compensationModel Context triple: [Herbalife, compensationModel, multi-level distributor commissions]
-
A.
compensationCategory
Indicates the type or classification of compensation associated with an entity, such as how or in what form payment or remuneration is provided.
-
B.
compensationPolicy
Indicates the rules or guidelines that govern how compensation (such as salary, bonuses, or benefits) is determined and provided.
-
C.
compensated
Indicates that one entity provides payment or some form of recompense to another entity in return for goods, services, or loss incurred.
-
D.
rewardModel
Indicates a relationship where one entity serves as a model or framework for assigning rewards or evaluating outcomes for another entity or process.
-
E.
payScale
Indicates the compensation level or salary range assigned to an entity, typically reflecting its relative pay or grade within a structured system.
- 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_69bd43dda32c8190938b37744ca270fc |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd655aceb081908100ffc0498fe183 |
completed | March 20, 2026, 3:18 p.m. |
| PD | Predicate disambiguation | batch_69bd6215864c8190b50ba0f63ba87d0c |
completed | March 20, 2026, 3:04 p.m. |
| PDg | Predicate description generation | batch_69bd655719dc8190beb6942f343a0ae7 |
completed | March 20, 2026, 3:18 p.m. |
Created at: March 20, 2026, 1:15 p.m.