Triple
T1647884
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SAP |
E35622
|
entity |
| Predicate | knownFor |
P22
|
FINISHED |
| Object | SAP SuccessFactors |
E35622
|
NE 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: SAP SuccessFactors | Statement: [SAP, knownFor, SAP SuccessFactors]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SAP SuccessFactors Context triple: [SAP, knownFor, SAP SuccessFactors]
-
A.
SAP
SAP was the former official currency of South Africa, used before the adoption of the South African rand.
-
B.
SAP
chosen
SAP is a leading global enterprise software company best known for its ERP solutions that help organizations manage business operations and customer relations.
-
C.
Ultimate Software
Ultimate Software was a leading American provider of cloud-based human capital management and payroll software solutions for businesses.
-
D.
Salesforce
Salesforce is a leading cloud-based customer relationship management (CRM) company known for its suite of enterprise applications for sales, service, marketing, and analytics.
-
E.
PeopleSoft
PeopleSoft is an enterprise software company best known for its human resources and financial management applications, later integrated into Oracle’s product portfolio.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69a8860568888190a32cd9f70acbba42 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a90a640ea88190822906da575d5165 |
completed | March 5, 2026, 4:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad60a4bd5481908b46f44364c15592 |
completed | March 8, 2026, 11:42 a.m. |
Created at: March 4, 2026, 7:29 p.m.