Triple
T37078432
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Independent Order of St. Luke |
E917775
|
entity |
| Predicate | typeOfBenefitSociety |
P13548
|
FINISHED |
| Object | life insurance society |
—
|
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: life insurance society | Statement: [Independent Order of St. Luke, typeOfBenefitSociety, life insurance society]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfBenefitSociety Context triple: [Independent Order of St. Luke, typeOfBenefitSociety, life insurance society]
-
A.
beneficeType
Indicates the specific category or kind of benefice (ecclesiastical office or endowed church position) associated with an entity.
-
B.
benefactionType
Indicates the specific kind or category of benefit, gift, or supportive action provided in a benefaction relationship.
-
C.
typeOfBenefitProvider
Indicates the specific category or kind of entity that provides a given benefit.
-
D.
benefitsOrganizationType
Indicates that something provides an advantage, support, or positive impact specifically to a particular type or category of organization.
-
E.
nonprofitType
chosen
Indicates the specific category or classification of a nonprofit organization based on its legal or functional type.
- 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_69f76e9771e08190a690834e3cd20654 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a0349d158b881908bfbdb501ea565ee |
completed | May 12, 2026, 3:40 p.m. |
| PD | Predicate disambiguation | batch_6a034750e3d48190a88ee3604a36b46d |
completed | May 12, 2026, 3:29 p.m. |
Created at: May 3, 2026, 4:14 p.m.