Triple

T8611482
Position Surface form Disambiguated ID Type / Status
Subject SD Association E203922 entity
Predicate hasMember P10 FINISHED
Object Canon E228413 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: Canon | Statement: [SD Association, hasMember, Canon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Canon
Context triple: [SD Association, hasMember, Canon]
  • A. Canon
    Canon is a structured set of hymns or chants used in Eastern Christian liturgical services, particularly within the Orthodox tradition.
  • B. Canon PIXMA chosen
    Canon PIXMA is a line of consumer and small-office inkjet printers from Canon known for combining high-quality photo printing with versatile document printing and scanning features.
  • C. Ricoh
    Ricoh is a Japanese multinational imaging and electronics company best known for its cameras, printers, copiers, and office equipment solutions.
  • D. Epson
    Epson is a Japanese electronics company best known for manufacturing printers, imaging equipment, and related information technology devices.
  • E. Kodak printers
    Kodak printers are consumer and professional printing devices produced under the Kodak brand, known for their photo-printing capabilities and integration with Kodak imaging technologies.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69ca832c23e4819095a9f3eea4a21828 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cc46fc31e08190aab5ab8f92f3315c completed March 31, 2026, 10:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69cebbc1d8a08190bbcf7c4cef0fe04d completed April 2, 2026, 6:56 p.m.
Created at: March 30, 2026, 6:25 p.m.