Triple
T23142656
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Naive Bayes classifier |
E577500
|
entity |
| Predicate | commonImplementation |
P3697
|
FINISHED |
| Object | Weka |
—
|
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: Weka | Statement: [Naive Bayes classifier, commonImplementation, Weka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Weka Context triple: [Naive Bayes classifier, commonImplementation, Weka]
-
A.
Rossum
Rossum is the surname of Emmy Rossum, an American actress and singer best known for her role as Fiona Gallagher on the television series "Shameless."
-
B.
Rossum
Rossum is a village in the municipality of Dinkelland in the province of Overijssel in the eastern Netherlands.
-
C.
Svm
Svm is the station code used to identify Svanemøllen railway station in Copenhagen’s public transport system.
-
D.
Apache Mahout
chosen
Apache Mahout is an open-source machine learning library designed to build scalable algorithms for clustering, classification, and recommendation on large datasets, often leveraging big data platforms.
-
E.
Sparx
Sparx is the loyal dragonfly companion and health indicator who follows Spyro throughout the Spyro the Dragon video game series.
- 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_69e245f8e6248190ba3d58e068b4dccb |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18ecb72fc8190a24e8f5756217a36 |
completed | April 29, 2026, 4:53 a.m. |
Created at: April 17, 2026, 4 p.m.