Triple

T75407
Position Surface form Disambiguated ID Type / Status
Subject Roma E1507 entity
Predicate hasDiaspora P2103 FINISHED
Object Mexico E346 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: Mexico | Statement: [Roma, hasDiaspora, Mexico]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mexico
Context triple: [Roma, hasDiaspora, Mexico]
  • A. Mexico chosen
    Mexico is a large North American country known for its rich pre-Columbian and colonial history, diverse cultures, and influential cuisine and arts.
  • B. Guatemala
    Guatemala is a Central American country known for its Mayan heritage, volcanic landscapes, and vibrant indigenous cultures.
  • C. Cuba
    Cuba is a Caribbean island nation known for its communist government, historic Havana architecture, classic cars, and influential music and culture.
  • D. El Salvador
    El Salvador is a Central American country known for being the smallest and most densely populated nation in the region, with a history of civil conflict and a recent push toward economic modernization and cryptocurrency adoption.
  • E. Veracruz
    Veracruz is a coastal state in eastern Mexico on the Gulf of Mexico, known for its major port city, rich colonial and indigenous history, and diverse geography ranging from tropical lowlands to high mountains.
  • 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_69a24c60d19c8190a1b6c105ca59ef5b completed Feb. 28, 2026, 2:01 a.m.
NER Named-entity recognition batch_69a24f1b99a48190aec004ecd49b4a0d completed Feb. 28, 2026, 2:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69a33e3a315881909a2b717ef20e4b17 completed Feb. 28, 2026, 7:12 p.m.
Created at: Feb. 28, 2026, 2:06 a.m.