Triple
T17521086
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TensorFlow Estimators |
E426678
|
entity |
| Predicate | hasExampleImplementation |
P127768
|
FINISHED |
| Object | LinearRegressor |
—
|
NE NERFINISHED |
How this triple was built (3 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: LinearRegressor | Statement: [TensorFlow Estimators, hasExampleImplementation, LinearRegressor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LinearRegressor Context triple: [TensorFlow Estimators, hasExampleImplementation, LinearRegressor]
-
A.
Regression
Regression is a 2015 psychological thriller film directed by Alejandro Amenábar, centered on a detective investigating a disturbing case of alleged satanic ritual abuse in 1990s Minnesota.
-
B.
LogisticRegression
LogisticRegression is a scikit-learn machine learning estimator that models the probability of class membership using a linear decision boundary with logistic (sigmoid) or related link functions.
-
C.
Bayesian linear regression
Bayesian linear regression is a statistical modeling approach that treats regression coefficients and predictions probabilistically by placing prior distributions on parameters and updating them with observed data.
-
D.
Linear Estimation
Linear Estimation is a foundational text in signal processing and control theory that systematically develops the theory and applications of optimal estimation, including Kalman filtering and related methods.
-
E.
LINEAR
LINEAR (Lincoln Near-Earth Asteroid Research) is an automated survey program that uses ground-based telescopes to discover and track near-Earth objects such as asteroids and comets.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: LinearRegressor Target entity description: LinearRegressor is a TensorFlow Estimator that implements linear regression models for predicting continuous values from input features.
-
A.
Regression
Regression is a 2015 psychological thriller film directed by Alejandro Amenábar, centered on a detective investigating a disturbing case of alleged satanic ritual abuse in 1990s Minnesota.
-
B.
LogisticRegression
LogisticRegression is a scikit-learn machine learning estimator that models the probability of class membership using a linear decision boundary with logistic (sigmoid) or related link functions.
-
C.
Bayesian linear regression
Bayesian linear regression is a statistical modeling approach that treats regression coefficients and predictions probabilistically by placing prior distributions on parameters and updating them with observed data.
-
D.
Linear Estimation
Linear Estimation is a foundational text in signal processing and control theory that systematically develops the theory and applications of optimal estimation, including Kalman filtering and related methods.
-
E.
LINEAR
LINEAR (Lincoln Near-Earth Asteroid Research) is an automated survey program that uses ground-based telescopes to discover and track near-Earth objects such as asteroids and comets.
- F. None of above. chosen
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_69d889de677081909b22d2657b1f0292 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e452d23cf08190925510344fa36f57 |
completed | April 19, 2026, 3:58 a.m. |
Created at: April 10, 2026, 5:49 a.m.