Triple

T23521538
Position Surface form Disambiguated ID Type / Status
Subject Diego Valeri E574519 entity
Predicate memberOfSportsTeam P330 FINISHED
Object San Lorenzo de Almagro NE NERFINISHED

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: San Lorenzo de Almagro | Statement: [Diego Valeri, memberOfSportsTeam, San Lorenzo de Almagro]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: San Lorenzo de Almagro
Context triple: [Diego Valeri, memberOfSportsTeam, San Lorenzo de Almagro]
  • A. San Lorenzo de Almagro chosen
    San Lorenzo de Almagro is a major Argentine sports club from Buenos Aires best known for its successful and passionately supported football team, one of the traditional "big five" in Argentine football.
  • B. San Lorenzo
    San Lorenzo is a historic church in the Italian town of Spello, known for its medieval architecture and religious significance.
  • C. San Lorenzo
    San Lorenzo is a municipality in the Suchitepéquez Department of southwestern Guatemala, known for its agricultural economy and rural communities.
  • D. San Lorenzo
    San Lorenzo is a town located in Peru's Loreto Department within the Amazon rainforest region.
  • E. San Lorenzo
    San Lorenzo is a rural municipality in Nicaragua’s central Boaco Department, known for its agricultural economy and small-town character.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e245bb3dcc8190ba9a2b35972b58d0 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f1aa873ad48190a86807bd4f26df82 completed April 29, 2026, 6:51 a.m.
Created at: April 17, 2026, 6:08 p.m.