Triple
T32884406
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leonard Wilf |
E841155
|
entity |
| Predicate | employerOrPrincipal |
P7
|
FINISHED |
| Object | family-owned real estate enterprises |
—
|
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: family-owned real estate enterprises | Statement: [Leonard Wilf, employerOrPrincipal, family-owned real estate enterprises]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employerOrPrincipal Context triple: [Leonard Wilf, employerOrPrincipal, family-owned real estate enterprises]
-
A.
employerOrPrimaryClient
Indicates that one entity serves as the main employer or principal client of another entity in a work or service relationship.
-
B.
employerIn
Indicates that one entity serves as the employer of another within a specified context, such as a location, organization, or time period.
-
C.
parentEmployer
Indicates that one organization is the direct or higher-level employer of another organization or entity.
-
D.
employerOrPartner
Indicates that one entity is either the employer of, or a business partner with, another entity.
-
E.
employer
chosen
Indicates a relationship where one entity hires, pays, and oversees the work of another entity.
- 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_69f349446e288190a70c05bcc4d81172 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f7764ab1fc81909f9348db87bd7692 |
completed | May 3, 2026, 4:22 p.m. |
| PD | Predicate disambiguation | batch_69f76905d9c88190b1ee810bc9ab644f |
completed | May 3, 2026, 3:25 p.m. |
Created at: May 1, 2026, 1:18 a.m.