Triple
T18178180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deep Learning with Python |
E435215
|
entity |
| Predicate | hasEdition |
P35
|
FINISHED |
| Object | Deep Learning with Python, Second Edition |
—
|
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: Deep Learning with Python, Second Edition | Statement: [Deep Learning with Python, hasEdition, Deep Learning with Python, Second Edition]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Deep Learning with Python, Second Edition Context triple: [Deep Learning with Python, hasEdition, Deep Learning with Python, Second Edition]
-
A.
"Deep Learning with Python"
chosen
"Deep Learning with Python" is a practical book that introduces deep learning concepts and techniques using the Keras library and the Python ecosystem, aimed at helping developers and researchers build and understand modern neural networks.
-
B.
Deep Learning (book)
Deep Learning (book) is a foundational textbook that systematically introduces the theory and practice of modern deep neural networks, co-authored by leading researchers including Yoshua Bengio.
-
C.
book "Deep Learning with Python"
"Deep Learning with Python" is a practical, example-driven book that introduces deep learning concepts and techniques using the Keras library and the Python ecosystem.
-
D.
TensorFlow in Practice Specialization
TensorFlow in Practice Specialization is an online deep learning program on Coursera that teaches practical TensorFlow skills for building and deploying neural network models.
-
E.
Deeplearning.ai
Deeplearning.ai is an online education company specializing in artificial intelligence and deep learning courses and resources.
- 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_69d8b90c7ec081909b4694ccecb449c6 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4df5b68f081908aac8210270f1499 |
completed | April 19, 2026, 1:57 p.m. |
Created at: April 10, 2026, 10:31 a.m.