Triple

T1621731
Position Surface form Disambiguated ID Type / Status
Subject Grant Wood E35045 entity
Predicate movement P81 FINISHED
Object Regionalism E34669 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: Regionalism | Statement: [Grant Wood, movement, Regionalism]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Regionalism
Context triple: [Grant Wood, movement, Regionalism]
  • A. Regionalism chosen
    Regionalism was an American art movement of the 1930s that emphasized realistic depictions of rural life and local landscapes, particularly in the Midwest.
  • B. Pan-Americanism
    Pan-Americanism is a political and cultural movement advocating cooperation, solidarity, and closer ties among the nations of the Americas.
  • C. Lokrahitya
    Lokrahitya is a literary work by renowned Bengali novelist and essayist Bankim Chandra Chattopadhyay, reflecting his engagement with Bengali literature and culture.
  • D. Pashtun nationalism
    Pashtun nationalism is a political and cultural movement advocating for the unity, rights, and self-determination of the Pashtun people across their traditional homelands in Afghanistan and Pakistan.
  • E. Nation
    Nation is a major public square and transportation hub in eastern Paris, France, known for its large roundabout, prominent monuments, and busy metro and RER interchange.
  • 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_69a886023194819080a3fccd6e325d0e completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a909b1fc788190b38c0aa4ccc2e953 completed March 5, 2026, 4:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad51d2cbb481908bc74cecdc023547 completed March 8, 2026, 10:39 a.m.
Created at: March 4, 2026, 7:28 p.m.