Triple
T4894175
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | U.S. credit unions |
E109632
|
entity |
| Predicate | commonBondType |
P49770
|
FINISHED |
| Object | employment-based |
—
|
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: employment-based | Statement: [U.S. credit unions, commonBondType, employment-based]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commonBondType Context triple: [U.S. credit unions, commonBondType, employment-based]
-
A.
collateralType
Indicates the kind or category of collateral associated with an obligation, agreement, or financial exposure.
-
B.
hasBondType
chosen
Indicates the specific kind of bond or connection that exists between two related entities.
-
C.
lenderType
Indicates the classification or category of the lender involved in a lending relationship (e.g., bank, individual, institution).
-
D.
typeOfTrust
Indicates the specific kind or category of trust that characterizes the relationship between entities.
-
E.
beneficiaryType
Indicates the type or category of beneficiary that receives or is intended to receive the benefit or outcome of an action or resource.
- 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_69bd4410bbf88190aad50d2451c863d6 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6ffabccc81909115ece1b04e2061 |
completed | March 20, 2026, 4:04 p.m. |
| PD | Predicate disambiguation | batch_69bd6c2e7b5c8190b8bf9d616dfa24f0 |
completed | March 20, 2026, 3:47 p.m. |
Created at: March 20, 2026, 1:28 p.m.