Triple
T18799310
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | statsmodels |
E459721
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object | statsmodels.sandbox |
—
|
NE NERFINISHED |
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: statsmodels.sandbox | Statement: [statsmodels, hasComponent, statsmodels.sandbox]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: statsmodels.sandbox Context triple: [statsmodels, hasComponent, statsmodels.sandbox]
-
A.
statsmodels
chosen
statsmodels is a Python library for statistical modeling and econometrics, providing tools for estimating and interpreting a wide range of statistical models and tests.
-
B.
Taxidea
Taxidea is a genus of mustelid mammals best known for the American badger, a burrowing carnivore native to North America.
-
C.
"Statistical Models in S"
"Statistical Models in S" is a foundational book that presents methods and practical guidance for implementing a wide range of statistical modeling techniques using the S programming environment.
-
D.
modelr
modelr is an R package that provides tools for modeling within the tidyverse ecosystem, simplifying the process of building, evaluating, and visualizing statistical models.
-
E.
Tobit model
The Tobit model is an econometric regression model designed for situations where the dependent variable is censored, allowing consistent estimation when observations are only partially observed beyond certain limits.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d398c7d4819091cb2f7e48948aeb |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5a02273b481909bc250144a0ace32 |
completed | April 20, 2026, 3:40 a.m. |
Created at: April 10, 2026, 11:53 a.m.