Triple
T7461225
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FRNs |
E176252
|
entity |
| Predicate | creditRiskLevel |
P3842
|
FINISHED |
| Object | backed by full faith and credit of the U.S. government |
—
|
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: backed by full faith and credit of the U.S. government | Statement: [FRNs, creditRiskLevel, backed by full faith and credit of the U.S. government]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: creditRiskLevel Context triple: [FRNs, creditRiskLevel, backed by full faith and credit of the U.S. government]
-
A.
riskLevel
chosen
Indicates the degree of potential harm, loss, or adverse outcome associated with a particular situation, action, or entity.
-
B.
riskType
Indicates the category or nature of risk associated with an entity, event, or relationship.
-
C.
riskModel
Indicates a relationship where an entity serves as or is associated with a model used to assess, quantify, or manage risk for another entity or situation.
-
D.
riskElement
Indicates that one entity is a risk-related component, factor, or contributor associated with another entity within a risk context.
-
E.
riskBasis
Indicates the underlying factor, condition, or rationale that forms the basis for assessing or assigning risk in a given context.
- 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_69c69f21632481908bf83f6c6da897e3 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f3d6cf8c8190a31cac121d151d78 |
completed | March 27, 2026, 9:17 p.m. |
| PD | Predicate disambiguation | batch_69c6f03bad9c8190bdd5abb86d37df47 |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:38 p.m.