Triple

T13996787
Position Surface form Disambiguated ID Type / Status
Subject Fortunio Bonanova E336717 entity
Predicate notableWork P4 FINISHED
Object Blood and Sand E172576 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: Blood and Sand | Statement: [Fortunio Bonanova, notableWork, Blood and Sand]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Blood and Sand
Context triple: [Fortunio Bonanova, notableWork, Blood and Sand]
  • A. Blood and Sand
    Blood and Sand is a 1922 silent drama film starring Rudolph Valentino as a rising bullfighter whose fame leads to personal and moral downfall.
  • B. Blood and Sand chosen
    Blood and Sand is a 1941 Technicolor drama film starring Tyrone Power and Rita Hayworth, renowned for its lavish production and tragic tale of a Spanish bullfighter.
  • C. Blood and Sand
    Blood and Sand is a notable work by Edward Snyder, best known as a dramatic play exploring themes of passion, betrayal, and moral conflict.
  • D. La malasangre
    La malasangre is a notable dramatic work associated with Argentine actress and director Cristina Banegas.
  • E. Black Mischief
    Black Mischief is a satirical novel by Evelyn Waugh that lampoons British imperialism and modernizing schemes in a fictional African kingdom.
  • 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_69d81c645c5c8190b1fd16a285a1b78a completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2eb68ba88190bfaf10777d607bf3 completed April 14, 2026, 12:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69fbac9d4a54819091c7efbeb4dcc5f7 completed May 6, 2026, 9:03 p.m.
Created at: April 9, 2026, 10:19 p.m.