Triple

T16912446
Position Surface form Disambiguated ID Type / Status
Subject Fernando E410234 entity
Predicate hasVariant P455 FINISHED
Object Fernand E338517 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: Fernand | Statement: [Fernando, hasVariant, Fernand]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fernand
Context triple: [Fernando, hasVariant, Fernand]
  • A. Fernand chosen
    Fernand is a given name, primarily used in French and other Romance-language contexts, that corresponds to the name Ferdinand.
  • B. Henri Rouart
    Henri Rouart was a French industrialist, art collector, and painter closely associated with the Impressionist movement and its circle of artists.
  • C. Pierre
    Pierre is a masculine given name of French origin that has been borne by numerous notable figures in history, arts, and science.
  • D. Pierre-Paul
    Pierre-Paul is a French given name most notably borne by Pierre-Paul Riquet, the 17th-century engineer who designed and built the Canal du Midi.
  • E. René
    René is a French given name commonly used for males and historically associated with several notable figures in politics, arts, and philosophy.
  • 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_69d886c7b1e481908c3766dfa8c13458 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3ca3e6b9481909fbaeb0bddd7e3b2 completed April 18, 2026, 6:15 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00c7bb4ac481909318d3d61a2d10e1 completed May 10, 2026, 6 p.m.
Created at: April 10, 2026, 5:30 a.m.