Triple

T126499
Position Surface form Disambiguated ID Type / Status
Subject Royal Spanish Academy E2561 entity
Predicate hasChair P377 FINISHED
Object Chair D E13991 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: Chair D | Statement: [Royal Spanish Academy, hasChair, Chair D]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chair D
Context triple: [Royal Spanish Academy, hasChair, Chair D]
  • A. Chair A chosen
    Chair A is one of the numbered academic seats of the Royal Spanish Academy, traditionally assigned to a distinguished scholar of the Spanish language.
  • B. The D
    "The D" is a popular nickname for Detroit, a major U.S. city known for its automotive industry, musical heritage, and role in American industrial history.
  • C. Dennis
    Dennis is a coastal town on Cape Cod in Massachusetts known for its beaches, historic charm, and popular summer tourism.
  • D. MacDouglas
    MacDouglas is a Scottish surname variant of Douglas, traditionally associated with clans and families of Scottish heritage.
  • E. Al Reser
    Al Reser was an American businessman and Oregon State University alumnus best known as the longtime head of Reser's Fine Foods and a major benefactor of OSU athletics.
  • 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_69a251b54ea88190b18281669f59b4c0 completed Feb. 28, 2026, 2:23 a.m.
NER Named-entity recognition batch_69a25761e9248190a7205bfc36cb5c45 completed Feb. 28, 2026, 2:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69a2aa453a8c81908d6d4d14aff8522e completed Feb. 28, 2026, 8:41 a.m.
Created at: Feb. 28, 2026, 2:27 a.m.