Triple
T6778091
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Production Credit Association |
E155608
|
entity |
| Predicate | typicalLoanPurpose |
P72742
|
FINISHED |
| Object | crop production expenses |
—
|
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: crop production expenses | Statement: [Production Credit Association, typicalLoanPurpose, crop production expenses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalLoanPurpose Context triple: [Production Credit Association, typicalLoanPurpose, crop production expenses]
-
A.
loanType
Indicates the specific category or kind of loan associated with an entity or transaction.
-
B.
additionalFundingPurpose
Indicates the specific reason or intended use for which additional funding is sought or allocated beyond the original amount.
-
C.
lenderType
Indicates the classification or category of the lender involved in a lending relationship (e.g., bank, individual, institution).
-
D.
collateralType
Indicates the kind or category of collateral associated with an obligation, agreement, or financial exposure.
-
E.
orderPurpose
Indicates that an order is placed with the specific purpose or intended use of the ordered item or service.
- 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_69c688162bf8819088b664b5c3b5be7a |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d2689d408190bc2c1ce4ae9c1b13 |
completed | March 27, 2026, 6:54 p.m. |
| PD | Predicate disambiguation | batch_69c6d095dcac8190bb9b943f50a7f885 |
completed | March 27, 2026, 6:46 p.m. |
| PDg | Predicate description generation | batch_69c6d182213c819086fcbbfd3d64d80b |
completed | March 27, 2026, 6:50 p.m. |
Created at: March 27, 2026, 2:13 p.m.