Triple
T17005134
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thrift Savings Plan |
E412549
|
entity |
| Predicate | loanTypes |
P9377
|
FINISHED |
| Object | general purpose loans |
—
|
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: general purpose loans | Statement: [Thrift Savings Plan, loanTypes, general purpose loans]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: loanTypes Context triple: [Thrift Savings Plan, loanTypes, general purpose loans]
-
A.
loanType
chosen
Indicates the specific category or kind of loan associated with an entity or transaction.
-
B.
lenderType
Indicates the classification or category of the lender involved in a lending relationship (e.g., bank, individual, institution).
-
C.
loanStructure
Indicates the specific terms, components, and repayment arrangement that define how a loan is organized between parties.
-
D.
loanSource
Indicates that one entity is the origin or provider of a loan extended to another entity.
-
E.
collateralType
Indicates the kind or category of collateral associated with an obligation, agreement, or financial exposure.
- 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_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. |
Created at: April 10, 2026, 5:32 a.m.