Triple

T4689735
Position Surface form Disambiguated ID Type / Status
Subject ATC E104004 entity
Predicate usedIn P98 FINISHED
Object Americas E17691 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: Americas | Statement: [ATC, usedIn, Americas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Americas
Context triple: [ATC, usedIn, Americas]
  • A. Americas chosen
    The Americas are the combined landmasses of North and South America, encompassing a vast region of diverse cultures, climates, and ecosystems in the Western Hemisphere.
  • B. North America
    North America is a large continent in the Northern and Western Hemispheres that includes countries such as the United States, Canada, and Mexico.
  • C. América
    América is a popular Mexican professional football club based in Mexico City, widely recognized as one of the most successful and supported teams in Liga MX.
  • D. Amerika
    Amerika is a novel by Franz Kafka that follows a young European immigrant’s surreal and often absurd experiences in the United States.
  • E. Latin America
    Latin America is a culturally diverse region of the Americas, spanning Mexico, Central and South America, and much of the Caribbean, where Romance languages—primarily Spanish and Portuguese—predominate.
  • 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_69bd43df91f481908e9add1b617b60ef completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd6399621881909aa8ffb1c27284e9 completed March 20, 2026, 3:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69be105a709c819083504fe1dc1612d8 completed March 21, 2026, 3:28 a.m.
Created at: March 20, 2026, 1:16 p.m.