Triple
T37546932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cramér–Lundberg model |
E933487
|
entity |
| Predicate | hasAssumptionType |
P194542
|
FINISHED |
| Object | classical risk model assumptions |
—
|
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: classical risk model assumptions | Statement: [Cramér–Lundberg model, hasAssumptionType, classical risk model assumptions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAssumptionType Context triple: [Cramér–Lundberg model, hasAssumptionType, classical risk model assumptions]
-
A.
hasAssistanceType
Indicates that one entity provides or is associated with a specific kind or category of assistance in relation to another entity.
-
B.
hasAssociatedType
Indicates that one entity is linked to another entity that specifies its type, category, or classification.
-
C.
haveType
Indicates that an entity belongs to or is classified under a specified type or category.
-
D.
hasCriterionType
Indicates that something is associated with or classified by a specific type of criterion used for evaluation or decision-making.
-
E.
hasAssemblyType
Indicates that an entity is associated with or classified by a specific type or method of assembly.
- 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_69f76eca55bc8190acf25741793d5dac |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fd783fed9c81909e792702636c4f1f |
completed | May 8, 2026, 5:44 a.m. |
| PD | Predicate disambiguation | batch_69fd7788e63c81909de22fdafcfe41c0 |
completed | May 8, 2026, 5:41 a.m. |
| PDg | Predicate description generation | batch_69fd783e9e5c819087dec7fefa03700d |
completed | May 8, 2026, 5:44 a.m. |
Created at: May 3, 2026, 4:17 p.m.