Triple
T25337590
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fraud Division |
E635316
|
entity |
| Predicate | publicSafetyRole |
P30198
|
FINISHED |
| Object | protecting the integrity of the insurance system |
—
|
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: protecting the integrity of the insurance system | Statement: [Fraud Division, publicSafetyRole, protecting the integrity of the insurance system]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: publicSafetyRole Context triple: [Fraud Division, publicSafetyRole, protecting the integrity of the insurance system]
-
A.
hasSafetyRole
chosen
Indicates that an entity holds a responsibility or function related to safety within a given context or system.
-
B.
protectionRole
Indicates that one entity serves a protective function or responsibility toward another entity or resource.
-
C.
publicSafetyAgencyType
Indicates the specific category or kind of public safety agency associated with an entity (e.g., police, fire, emergency medical services).
-
D.
safetyContext
Indicates the circumstances, conditions, or environment that affect how safe an action, object, or situation is.
-
E.
safetyProfile
Indicates the overall level and characteristics of risk or harm associated with something, typically summarizing how safe it is under specified conditions.
- 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_69e75a99bd6481909476115b35b9a8e4 |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f657f653448190a945b4751af8507d |
completed | May 2, 2026, 8 p.m. |
| PD | Predicate disambiguation | batch_69f6575ba12081909396036f78757a76 |
completed | May 2, 2026, 7:58 p.m. |
Created at: April 21, 2026, 1:32 p.m.