Triple
T277378
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SAS |
E5277
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object | SAS/EM (Enterprise Miner) |
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/EM (Enterprise Miner) | Statement: [SAS, hasComponent, SAS/EM (Enterprise Miner)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SAS/EM (Enterprise Miner) Context triple: [SAS, hasComponent, SAS/EM (Enterprise Miner)]
-
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.
Tableau
Tableau is a widely used data visualization and business intelligence software platform that enables users to analyze, explore, and present data through interactive dashboards and reports.
-
D.
SAP
SAP is a leading global enterprise software company best known for its ERP solutions that help organizations manage business operations and customer relations.
-
E.
Siebel
Siebel is a surname most prominently associated with Jennifer Siebel Newsom, an American documentary filmmaker and the First Partner of California.
- 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_69a399a5913c819082fac6bb344bd585 |
completed | March 1, 2026, 1:43 a.m. |
Created at: Feb. 28, 2026, 2:59 a.m.