Triple

T17488829
Position Surface form Disambiguated ID Type / Status
Subject Grandpere's Eiffel Tower E425845 entity
Predicate fictionalResident P7550 FINISHED
Object Grandpere NE NERFINISHED

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: Grandpere | Statement: [Grandpere's Eiffel Tower, fictionalResident, Grandpere]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Grandpere
Context triple: [Grandpere's Eiffel Tower, fictionalResident, Grandpere]
  • A. Grandpere chosen
    Grandpere is a fictional character known for owning a whimsical version of the Eiffel Tower in a story or animated setting.
  • B. Grand-Mère
    Grand-Mère is a town in Quebec, Canada, known historically for its hydroelectric development and pulp and paper industry along the Saint-Maurice River.
  • C. Grand Popo
    Grand Popo is a historic coastal town in present-day Benin that served as an important trading center during the Atlantic slave trade.
  • D. Pere
    Pere is a masculine given name of Catalan origin, commonly used in Catalonia and equivalent to the English name Peter.
  • E. Pere
    Pere is the traditional royal title held by the monarch of the Gbaramatu Kingdom in Nigeria’s Niger Delta region.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d889dccf7481909264a1844a2e9100 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e451d461308190844c143dcfb61fac completed April 19, 2026, 3:53 a.m.
Created at: April 10, 2026, 5:48 a.m.