Triple

T3727523
Position Surface form Disambiguated ID Type / Status
Subject Avicenna E81784 entity
Predicate workCharacterization P44666 FINISHED
Object The Canon of Medicine served as a standard medical text in Europe and the Islamic world for centuries E311976 NE FINISHED

How this triple was built (3 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: The Canon of Medicine served as a standard medical text in Europe and the Islamic world for centuries | Statement: [Avicenna, workCharacterization, The Canon of Medicine served as a standard medical text in Europe and the Islamic world for centuries]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: The Canon of Medicine served as a standard medical text in Europe and the Islamic world for centuries
Context triple: [Avicenna, workCharacterization, The Canon of Medicine served as a standard medical text in Europe and the Islamic world for centuries]
  • A. The Canon of Medicine chosen
    The Canon of Medicine is a seminal 11th-century medical encyclopedia by Avicenna that systematized Greco-Arabic medical knowledge and served as a standard medical text in both the Islamic world and Europe for centuries.
  • B. Syriac medical tradition
    The Syriac medical tradition was a late antique and early medieval body of medical knowledge, largely transmitted in the Syriac language, that preserved and adapted Greco-Roman medicine and served as a crucial conduit to later Islamic medical scholarship.
  • C. Persian medicine
    Persian medicine is a traditional medical system that developed in the Persian cultural sphere, integrating ancient Greek, Indian, and local practices into a comprehensive theory of health, disease, and treatment.
  • D. Kitab al-Tibb al-Nabawi (Prophetic medicine works)
    Kitab al-Tibb al-Nabawi refers to classical Islamic treatises that compile and explain the health-related sayings and practices attributed to the Prophet Muhammad, blending religious guidance with medical knowledge.
  • E. Galenic medicine
    Galenic medicine is an ancient medical system based on the theories of the Greek physician Galen, emphasizing humoral balance and systematic clinical observation, which profoundly shaped later medical traditions.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: workCharacterization
Context triple: [Avicenna, workCharacterization, The Canon of Medicine served as a standard medical text in Europe and the Islamic world for centuries]
  • A. workCharacterType
    Indicates that a work involves or features a character of a specified type or role.
  • B. workDescribedIn
    Indicates that a work (such as a publication or document) provides a description or account of the referenced entity.
  • C. ruleCharacterization
    Indicates that one rule is described, defined, or characterized in terms of another rule or set of rules.
  • D. workDescribedAs chosen
    Indicates that one entity’s work, project, or output is characterized, labeled, or referred to by a particular description, title, or phrase.
  • E. workBasedOnThisCharacter
    Indicates that a creative work is based on, inspired by, or derived from the referenced character.
  • F. None of above.

Provenance (4 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_69ad8b1b7ef081908d2d381bbf54985a completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcaf7a6908190bd0c3bb5c55ab9ee completed March 8, 2026, 7:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4db1102a08190b5965c474dfab6db completed March 14, 2026, 3:50 a.m.
PD Predicate disambiguation batch_69adc0452f5081909c79e114a86cce8c completed March 8, 2026, 6:30 p.m.
Created at: March 8, 2026, 3:34 p.m.