Triple

T17520696
Position Surface form Disambiguated ID Type / Status
Subject Support Vector Machine E426671 entity
Predicate implementedIn P2539 FINISHED
Object R e1071 package
The R e1071 package is a widely used R library that provides implementations of support vector machines and other machine learning and statistical tools.
 E1274342 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: R e1071 package | Statement: [Support Vector Machine, implementedIn, R e1071 package]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: R e1071 package
Context triple: [Support Vector Machine, implementedIn, R e1071 package]
  • A. E71
    The E71 is a Nokia smartphone from the Eseries line, known for its full QWERTY keyboard, slim metal design, and business-oriented features.
  • B. e-RS
    e-RS is the abbreviation for the NHS e‑Referral Service, a digital system used in the UK to manage and book patient referrals electronically.
  • C. R10
    R10 is a nickname for Ronaldinho, the legendary Brazilian attacking midfielder and forward renowned for his flair, creativity, and skillful play.
  • D. E70
    E70 is BMW's internal model designation for the second-generation X5 mid-size luxury SUV produced from 2006 to 2013.
  • E. E70
    E70 is a Nokia Eseries smartphone known for its fold-out full QWERTY keyboard and business-oriented features.
  • 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: R e1071 package
Triple: [Support Vector Machine, implementedIn, R e1071 package]
Generated description
The R e1071 package is a widely used R library that provides implementations of support vector machines and other machine learning and statistical tools.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: R e1071 package
Target entity description: The R e1071 package is a widely used R library that provides implementations of support vector machines and other machine learning and statistical tools.
  • A. E71
    The E71 is a Nokia smartphone from the Eseries line, known for its full QWERTY keyboard, slim metal design, and business-oriented features.
  • B. e-RS
    e-RS is the abbreviation for the NHS e‑Referral Service, a digital system used in the UK to manage and book patient referrals electronically.
  • C. R10
    R10 is a nickname for Ronaldinho, the legendary Brazilian attacking midfielder and forward renowned for his flair, creativity, and skillful play.
  • D. E70
    E70 is BMW's internal model designation for the second-generation X5 mid-size luxury SUV produced from 2006 to 2013.
  • E. E70
    E70 is a Nokia Eseries smartphone known for its fold-out full QWERTY keyboard and business-oriented features.
  • 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_69d889de677081909b22d2657b1f0292 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e452d23cf08190925510344fa36f57 completed April 19, 2026, 3:58 a.m.
NED1 Entity disambiguation (via context triple) batch_6a01c94237d08190bb1f874735c87803 completed May 11, 2026, 12:19 p.m.
NEDg Description generation batch_6a01cabea2b48190a690b17a88d45b40 completed May 11, 2026, 12:25 p.m.
NED2 Entity disambiguation (via description) batch_6a01cefa08f8819086cb86ce22193baa completed May 11, 2026, 12:43 p.m.
Created at: April 10, 2026, 5:49 a.m.