Triple
T2473038
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Northwestern Mutual |
E55019
|
entity |
| Predicate | typeOfInsuranceOffered |
P12185
|
FINISHED |
| Object | individual life insurance |
—
|
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: individual life insurance | Statement: [Northwestern Mutual, typeOfInsuranceOffered, individual life insurance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfInsuranceOffered Context triple: [Northwestern Mutual, typeOfInsuranceOffered, individual life insurance]
-
A.
insuranceType
chosen
Indicates the specific category or kind of insurance coverage associated with an entity or relationship.
-
B.
typeOfCoverage
Indicates the specific kind or category of coverage that applies in a given context (such as insurance, service, or protection).
-
C.
typeOfDiscriminationCovered
Indicates that a particular kind or category of discriminatory behavior is included within the scope of protections, rules, or analysis.
-
D.
offeringType
Indicates the category or nature of what is being offered in a transaction or interaction (e.g., product, service, or other type of offering).
-
E.
awardedForCoverageType
Indicates that something (such as an award, benefit, or recognition) is granted specifically in relation to a particular type or category of coverage.
- 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_69ab49e279e88190ab10d7248aea9d11 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd1eb3be481908fa7c6b8f1c78209 |
completed | March 7, 2026, 7:21 a.m. |
| PD | Predicate disambiguation | batch_69abd0b5e3d481909a5cbc4a96edd24f |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:45 p.m.