Triple
T277365
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SAS |
E5277
|
entity |
| Predicate | developer |
P73
|
FINISHED |
| Object | SAS Institute |
E5277
|
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: SAS Institute | Statement: [SAS, developer, SAS Institute]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SAS Institute Context triple: [SAS, developer, SAS Institute]
-
A.
SAS
chosen
SAS is a widely used statistical software suite for advanced analytics, business intelligence, data management, and predictive modeling.
-
B.
SAS
SAS is the School of Arts and Sciences at the University of Pennsylvania, encompassing the university’s core liberal arts and sciences departments and programs.
-
C.
Siebel Systems
Siebel Systems was a leading enterprise software company best known for pioneering customer relationship management (CRM) solutions for large organizations.
-
D.
PeopleSoft
PeopleSoft is an enterprise software company best known for its human resources and financial management applications, later integrated into Oracle’s product portfolio.
-
E.
Statistical Research Center
The Statistical Research Center is a unit of the American Institute of Physics that conducts and disseminates data-driven studies on education, employment, and demographics in the physical sciences community.
- 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_69a257e6c8788190987dfe705ca2912a |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25ded68c88190b1fc595ce329aeb9 |
completed | Feb. 28, 2026, 3:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a39153528c8190a0f1e69a3f94b305 |
completed | March 1, 2026, 1:07 a.m. |
Created at: Feb. 28, 2026, 2:59 a.m.