Triple
T8912690
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fisher's exact test |
E212220
|
entity |
| Predicate | implementedIn |
P2539
|
FINISHED |
| Object |
Stata
Stata is a commercial statistical software package widely used in research for data management, advanced statistical analysis, and graphical visualization.
|
E765762
|
NE FINISHED |
How this triple was built (4 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: Stata | Statement: [Fisher's exact test, implementedIn, Stata]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stata Context triple: [Fisher's exact test, implementedIn, Stata]
-
A.
IBM SPSS Statistics
IBM SPSS Statistics is a widely used software package for advanced statistical analysis, data management, and predictive analytics in business, research, and academia.
-
B.
sas
sas is the ISO 639-3 code for the Sasak language spoken primarily on the Indonesian island of Lombok.
-
C.
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.
-
D.
SAS
SAS is a widely used statistical software suite for advanced analytics, business intelligence, data management, and predictive modeling.
-
E.
SAS
SAS is a high-speed, point-to-point serial interface standard commonly used to connect enterprise storage devices like hard drives and solid-state drives to servers.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Stata Triple: [Fisher's exact test, implementedIn, Stata]
Generated description
Stata is a commercial statistical software package widely used in research for data management, advanced statistical analysis, and graphical visualization.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Stata Target entity description: Stata is a commercial statistical software package widely used in research for data management, advanced statistical analysis, and graphical visualization.
-
A.
IBM SPSS Statistics
IBM SPSS Statistics is a widely used software package for advanced statistical analysis, data management, and predictive analytics in business, research, and academia.
-
B.
sas
sas is the ISO 639-3 code for the Sasak language spoken primarily on the Indonesian island of Lombok.
-
C.
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.
-
D.
SAS
SAS is a widely used statistical software suite for advanced analytics, business intelligence, data management, and predictive modeling.
-
E.
SAS
SAS is a high-speed, point-to-point serial interface standard commonly used to connect enterprise storage devices like hard drives and solid-state drives to servers.
- F. None of above. chosen
Provenance (5 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_69ca8393b1808190bd4336787ffa2c40 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6525d1408190a76522d7c4ac37da |
completed | April 1, 2026, 12:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfba3c92c481909589e6a3c9469136 |
completed | April 3, 2026, 1:01 p.m. |
| NEDg | Description generation | batch_69cfbabf33a08190a18d13b9078c00e2 |
completed | April 3, 2026, 1:03 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cfbba71a948190afc03a1df9e5777c |
completed | April 3, 2026, 1:07 p.m. |
Created at: March 30, 2026, 6:56 p.m.