Triple
T8019643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Silverado Savings and Loan collapse |
E186705
|
entity |
| Predicate | natureOfLosses |
P8356
|
FINISHED |
| Object | large loan losses on commercial real-estate projects |
—
|
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: large loan losses on commercial real-estate projects | Statement: [Silverado Savings and Loan collapse, natureOfLosses, large loan losses on commercial real-estate projects]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: natureOfLosses Context triple: [Silverado Savings and Loan collapse, natureOfLosses, large loan losses on commercial real-estate projects]
-
A.
losses
chosen
Indicates that an entity experiences a decrease in value, quantity, or advantage as a result of some event or comparison.
-
B.
causedLossOf
Indicates that one entity brought about or was responsible for another entity experiencing a loss.
-
C.
significantLoss
Indicates that an entity has experienced a major or substantial decrease in value, quantity, or status beyond a normal or minor loss.
-
D.
regionOfLoss
Indicates the anatomical or spatial area where a loss, damage, or deficit occurs or is localized.
-
E.
onlyProfessionalLossTo
Indicates that one entity is the only opponent to whom the other has ever lost in a professional 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_69ca82ac7fc081909b1398cf025423af |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3e8bc90081909f6f5878e6f1f241 |
completed | March 31, 2026, 3:24 a.m. |
| PD | Predicate disambiguation | batch_69cb049253d08190bafcecfde493ab8b |
completed | March 30, 2026, 11:17 p.m. |
Created at: March 30, 2026, 5:20 p.m.