Triple
T18300500
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ray |
E438345
|
entity |
| Predicate | integratesWith |
P1075
|
FINISHED |
| Object | XGBoost |
—
|
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: XGBoost | Statement: [Ray, integratesWith, XGBoost]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: XGBoost Context triple: [Ray, integratesWith, XGBoost]
-
A.
XGBoost
chosen
XGBoost is a high-performance, open-source gradient boosting library widely used for structured/tabular machine learning tasks such as classification and regression.
-
B.
LightGBM
LightGBM is a high-performance, gradient boosting framework based on decision trees, designed for speed and efficiency in large-scale machine learning tasks.
-
C.
XGB
XGB is the IATA station code assigned to Paris-Austerlitz railway station in Paris, France.
-
D.
CatBoost
CatBoost is an open-source gradient boosting library developed by Yandex, optimized for handling categorical features and delivering high-performance machine learning models.
-
E.
RandomForestClassifier
RandomForestClassifier is a popular ensemble machine learning algorithm in scikit-learn that builds multiple decision trees and aggregates their predictions for robust classification.
- 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_69d8b915e3e881909125d760c15d0c29 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e5017e88cc8190a969eb628ca1b496 |
completed | April 19, 2026, 4:23 p.m. |
Created at: April 10, 2026, 10:35 a.m.