Triple
T17005088
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roth IRA |
E412548
|
entity |
| Predicate | ownerMustHaveCompensation |
P126144
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Roth IRA, ownerMustHaveCompensation, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ownerMustHaveCompensation Context triple: [Roth IRA, ownerMustHaveCompensation, yes]
-
A.
compensationReason
Indicates the reason or cause for which compensation is granted, requested, or applied between entities.
-
B.
compensationIncludes
Indicates that a specified form of payment or benefit is part of the overall compensation provided in a given context.
-
C.
compensationPolicy
Indicates the rules or guidelines that govern how compensation (such as salary, bonuses, or benefits) is determined and provided.
-
D.
compensationModel
Indicates the type or structure of payment or rewards provided in exchange for work, services, or performance.
-
E.
providedCompensationAmount
Indicates the specific amount of compensation that was given or agreed to be given in relation to an action, event, or obligation.
- 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_69d886cb581c8190ab05f4b429c9cd85 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d382400c819092ec0ca0de815888 |
completed | April 18, 2026, 6:54 p.m. |
| PD | Predicate disambiguation | batch_69e35d552bc08190af17ef7659e094ef |
completed | April 18, 2026, 10:30 a.m. |
| PDg | Predicate description generation | batch_69e378e037c88190935d732e0f10d5d7 |
completed | April 18, 2026, 12:28 p.m. |
Created at: April 10, 2026, 5:32 a.m.