Triple

T16333993
Position Surface form Disambiguated ID Type / Status
Subject María Isabel Verdú Rollán E396627 entity
Predicate hasFamilyName P18 FINISHED
Object Rollán E357343 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: Rollán | Statement: [María Isabel Verdú Rollán, hasFamilyName, Rollán]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rollán
Context triple: [María Isabel Verdú Rollán, hasFamilyName, Rollán]
  • A. Rollán chosen
    Rollán is the Spanish family name of actress Maribel Verdú, known for her prominent roles in Spanish and international cinema.
  • B. Rolando
    Rolando is a masculine given name, commonly used in Romance-language countries, that is a variant of the name Orlando/Roland.
  • C. Guillermón
    Guillermón was the popular nickname of Cuban independence general Guillermón Moncada, a prominent Afro-Cuban military leader in the wars against Spanish colonial rule.
  • D. Blasco
    Blasco is a masculine given name of Spanish origin, historically borne by notable figures such as colonial administrators and writers.
  • E. Baltasar
    Baltasar is a variant of the name Belshazzar, historically associated with the last king of Babylon mentioned in the biblical Book of Daniel.
  • 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_69d87f255b788190a400eba031dd85d8 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e2c4e1da1081909bec6e77e6109dce completed April 17, 2026, 11:40 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0026173dc081909e00f6647d1f68b3 completed May 10, 2026, 6:30 a.m.
Created at: April 10, 2026, 5:07 a.m.