Triple
T25064694
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SREN |
E627752
|
entity |
| Predicate | associatedCompanyPrimaryBusinessLine |
P16009
|
FINISHED |
| Object | property and casualty reinsurance |
—
|
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: property and casualty reinsurance | Statement: [SREN, associatedCompanyPrimaryBusinessLine, property and casualty reinsurance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedCompanyPrimaryBusinessLine Context triple: [SREN, associatedCompanyPrimaryBusinessLine, property and casualty reinsurance]
-
A.
associatedCompanyMainProductLine
Indicates that a company’s primary or main line of products is linked or related to a specified product category or product line.
-
B.
associatedCompanyBusinessSegment
Indicates that a company is linked to a specific business segment through its operations, products, or services.
-
C.
hasMajorBusinessLine
chosen
Indicates that an entity conducts a primary or significant line of business in a specified area, sector, or activity.
-
D.
relatedCompanyType
Indicates the type or nature of the relationship that one company has to another company.
-
E.
associatedCompanySpecialization
Indicates that a company is linked to a particular area of specialization or expertise.
- 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_69e2ff2d71dc8190b4758e57d643cbe4 |
completed | April 18, 2026, 3:49 a.m. |
| NER | Named-entity recognition | batch_69f7221dc9a88190bb8194fcc29c42bc |
completed | May 3, 2026, 10:23 a.m. |
| PD | Predicate disambiguation | batch_69f72153a9188190b02adc84e1be4af8 |
completed | May 3, 2026, 10:20 a.m. |
Created at: April 18, 2026, 6:10 a.m.