Triple
T7893580
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rony Kahan |
E183294
|
entity |
| Predicate | previousCompanyFocus |
P79615
|
FINISHED |
| Object | financial sector job listings |
—
|
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: financial sector job listings | Statement: [Rony Kahan, previousCompanyFocus, financial sector job listings]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: previousCompanyFocus Context triple: [Rony Kahan, previousCompanyFocus, financial sector job listings]
-
A.
previousCorporateAffiliation
Indicates that an entity was formerly employed by, associated with, or part of a specified corporate organization before its current status or affiliation.
-
B.
employerFocus
Indicates that an employer directs particular attention, resources, or priority toward a specific subject, group, or area.
-
C.
formerEmployer
Indicates that one entity previously employed the other but no longer does so.
-
D.
underlyingCompanyBusinessFocus
Indicates the primary industry, sector, or type of business activity that the underlying company is focused on.
-
E.
organizationFocus
Indicates the primary area of activity, mission, or specialization that an organization is oriented toward or concentrated on.
- F. None of above. chosen
Provenance (4 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_69ca828c474c8190a254d6499871eaff |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a008fb88190a039fec40483ab93 |
completed | March 31, 2026, 3:05 a.m. |
| PD | Predicate disambiguation | batch_69cae92d94448190b4425bbfb64c658c |
completed | March 30, 2026, 9:20 p.m. |
| PDg | Predicate description generation | batch_69caf786ec748190b6347b0c94335550 |
completed | March 30, 2026, 10:21 p.m. |
Created at: March 30, 2026, 5:01 p.m.