Triple

T3771647
Position Surface form Disambiguated ID Type / Status
Subject Antonio Luna E83211 entity
Predicate givenName P17 FINISHED
Object Antonio E56351 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: Antonio | Statement: [Antonio Luna, givenName, Antonio]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Antonio
Context triple: [Antonio Luna, givenName, Antonio]
  • A. Antonio chosen
    Antonio is a masculine given name of Latin origin, widely used in Italian, Spanish, and Portuguese-speaking cultures.
  • B. Antonio Vandone di Cortemilia
    Antonio Vandone di Cortemilia was an Italian architect known for designing the Mogadishu Cathedral in Somalia during the colonial era.
  • C. Lorenzo
    Lorenzo is a masculine given name of Italian origin, historically borne by notable figures such as the Renaissance humanist Lorenzo Valla.
  • D. Bernardo Morando
    Bernardo Morando was a 16th-century Italian architect best known for designing the Renaissance ideal city of Zamość in Poland.
  • E. Ignazio
    Ignazio is an Italian given name, cognate to Ignacy and typically associated with the Latin-rooted names Ignatius and Ignacio.
  • 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_69ad8b235e608190b5a2b1d1bfcef50b completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcc3219b881908a2f82126f9a679d completed March 8, 2026, 7:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4e52bb2d08190b457dd517ff366d7 completed March 14, 2026, 4:33 a.m.
Created at: March 8, 2026, 3:36 p.m.