Triple

T19562373
Position Surface form Disambiguated ID Type / Status
Subject Mariano Boedo E489485 entity
Predicate givenName P17 FINISHED
Object Mariano E119168 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: Mariano | Statement: [Mariano Boedo, givenName, Mariano]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mariano
Context triple: [Mariano Boedo, givenName, Mariano]
  • A. Mariano chosen
    Mariano is a masculine given name of Spanish and Portuguese origin, commonly used in various Spanish-speaking and Latin cultures.
  • B. Mariano Roque Alonso
    Mariano Roque Alonso is a city in Paraguay known for its strategic location near the capital Asunción and its role as a commercial and industrial hub.
  • C. Mariano Osorio
    Mariano Osorio was a Spanish military officer best known for leading royalist forces against Chilean patriots during the Chilean War of Independence.
  • D. Filiberto
    Filiberto was a Puerto Rican nationalist and militant leader associated with the Puerto Rican independence movement.
  • E. Mariano Casanova
    Mariano Casanova was a Chilean Catholic archbishop and influential church leader known for his role in shaping modern Catholic education in Chile.
  • 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_69d8e8dc5d8c8190a6d7bd8864f43ca0 completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e63f74fdb08190852461b5d5c954ac completed April 20, 2026, 3 p.m.
NED1 Entity disambiguation (via context triple) batch_6a077ed4f3148190aee5530e31447d4e completed May 15, 2026, 8:15 p.m.
Created at: April 10, 2026, 1:42 p.m.