Triple
T9201194
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norman H. Nie |
E220841
|
entity |
| Predicate | coCreated |
P1858
|
FINISHED |
| Object | SPSS |
E699684
|
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: SPSS | Statement: [Norman H. Nie, coCreated, SPSS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SPSS Context triple: [Norman H. Nie, coCreated, SPSS]
-
A.
IBM SPSS Statistics
chosen
IBM SPSS Statistics is a widely used software package for advanced statistical analysis, data management, and predictive analytics in business, research, and academia.
-
B.
IBM SPSS Modeler
IBM SPSS Modeler is a visual data science and machine learning tool that enables users to build, test, and deploy predictive models without extensive programming.
-
C.
Stata
Stata is a commercial statistical software package widely used in research for data management, advanced statistical analysis, and graphical visualization.
-
D.
SAS
SAS is a widely used statistical software suite for advanced analytics, business intelligence, data management, and predictive modeling.
-
E.
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.
- 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_69ca83e8e9248190862cf3e41693b310 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccd9429b448190a078e9cdfedd4918 |
completed | April 1, 2026, 8:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d05c451210819091188151d799e4cb |
completed | April 4, 2026, 12:33 a.m. |
Created at: March 30, 2026, 7:25 p.m.