Triple

T27762135
Position Surface form Disambiguated ID Type / Status
Subject fastai E701495 entity
Predicate relatedTo P37 FINISHED
Object Practical Deep Learning for Coders
Practical Deep Learning for Coders is a popular fast.ai course that teaches hands-on deep learning techniques to programmers using high-level tools and real-world projects.
 E1790151 NE FINISHED

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: Practical Deep Learning for Coders | Statement: [fastai, relatedTo, Practical Deep Learning for Coders]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Practical Deep Learning for Coders
Triple: [fastai, relatedTo, Practical Deep Learning for Coders]
Generated description
Practical Deep Learning for Coders is a popular fast.ai course that teaches hands-on deep learning techniques to programmers using high-level tools and real-world projects.

Provenance (5 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_69ef6a5193808190816eb7d0020b2d87 completed April 27, 2026, 1:53 p.m.
NER Named-entity recognition batch_69f6376620888190bade1617f8c45ba1 completed May 2, 2026, 5:41 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12ecb34ae081909234f568f91bd1ae completed May 24, 2026, 12:18 p.m.
NEDg Description generation batch_6a12f0d3217c819082e4210de9b042f1 completed May 24, 2026, 12:36 p.m.
NED2 Entity disambiguation (via description) batch_6a12f202724481909acd0b6d31be8367 completed May 24, 2026, 12:41 p.m.
Created at: April 27, 2026, 4:28 p.m.