Triple

T816507
Position Surface form Disambiguated ID Type / Status
Subject scikit-learn E17661 entity
Predicate hasConcept P531 FINISHED
Object SVC
SVC is scikit-learn’s implementation of a Support Vector Machine classifier used for supervised learning tasks such as binary and multiclass classification.
 E97071 NE FINISHED

How this triple was built (4 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: SVC | Statement: [scikit-learn, hasConcept, SVC]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SVC
Context triple: [scikit-learn, hasConcept, SVC]
  • A. SVR
    SVR is the set of post-nominal letters used to denote recipients of the Order of the White Rose of Finland.
  • B. SVR
    SVR is Russia’s primary foreign intelligence service, which succeeded the Soviet-era KGB’s external intelligence functions after the USSR’s dissolution.
  • C. SCC
    SCC is the commonly used abbreviation for the MIT Schwarzman College of Computing, an interdisciplinary hub for computing and AI research and education.
  • D. SCC
    SCC is the commonly used abbreviation for the Supreme Court of Canada, the country's highest judicial authority.
  • E. CHED
    CHED is the commonly used abbreviation for the Division of Chemical Education, a professional organization focused on advancing the teaching and learning of chemistry.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: SVC
Triple: [scikit-learn, hasConcept, SVC]
Generated description
SVC is scikit-learn’s implementation of a Support Vector Machine classifier used for supervised learning tasks such as binary and multiclass classification.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SVC
Target entity description: SVC is scikit-learn’s implementation of a Support Vector Machine classifier used for supervised learning tasks such as binary and multiclass classification.
  • A. SVR
    SVR is Russia’s primary foreign intelligence service, which succeeded the Soviet-era KGB’s external intelligence functions after the USSR’s dissolution.
  • B. SVR
    SVR is the set of post-nominal letters used to denote recipients of the Order of the White Rose of Finland.
  • C. SCC
    SCC is the commonly used abbreviation for the Supreme Court of Canada, the country's highest judicial authority.
  • D. SCC
    SCC is the commonly used abbreviation for the MIT Schwarzman College of Computing, an interdisciplinary hub for computing and AI research and education.
  • E. CHED
    CHED is the commonly used abbreviation for the Division of Chemical Education, a professional organization focused on advancing the teaching and learning of chemistry.
  • F. None of above. chosen

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_69a4937bcaac8190a322524ac6f45a5a completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4ab621d2c819083f10bff4f66c482 completed March 1, 2026, 9:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69a76d8d1a448190be8494fa2776615a completed March 3, 2026, 11:23 p.m.
NEDg Description generation batch_69a78bd0a1d48190907434a17853dfb1 completed March 4, 2026, 1:33 a.m.
NED2 Entity disambiguation (via description) batch_69a78c3a57d88190a994ed44bcb2d8d1 completed March 4, 2026, 1:34 a.m.
Created at: March 1, 2026, 7:38 p.m.