Triple
T6230721
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Federal Work-Study |
E139344
|
entity |
| Predicate | compensationForm |
P14176
|
FINISHED |
| Object | wages |
—
|
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: wages | Statement: [Federal Work-Study, compensationForm, wages]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: compensationForm Context triple: [Federal Work-Study, compensationForm, wages]
-
A.
compensationModel
Indicates the type or structure of payment or rewards provided in exchange for work, services, or performance.
-
B.
compensationCategory
chosen
Indicates the type or classification of compensation associated with an entity, such as how or in what form payment or remuneration is provided.
-
C.
compensationPolicy
Indicates the rules or guidelines that govern how compensation (such as salary, bonuses, or benefits) is determined and provided.
-
D.
compensated
Indicates that one entity provides payment or some form of recompense to another entity in return for goods, services, or loss incurred.
-
E.
benefitForm
Indicates that one entity is a specific form, type, or variant in which a benefit is provided or realized for another entity.
- 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_69c008afd3148190b71e9eaa60420dd1 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c062ec5be4819084d6df2e8dd2a542 |
completed | March 22, 2026, 9:45 p.m. |
| PD | Predicate disambiguation | batch_69c055ffdf54819086d987d646e44ff5 |
completed | March 22, 2026, 8:50 p.m. |
Created at: March 22, 2026, 4:22 p.m.