Triple

T9099639
Position Surface form Disambiguated ID Type / Status
Subject Jairzinho E218119 entity
Predicate givenName P17 FINISHED
Object Jair E105696 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: Jair | Statement: [Jairzinho, givenName, Jair]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jair
Context triple: [Jairzinho, givenName, Jair]
  • A. Jair chosen
    Jair is a minor biblical judge of Israel mentioned in the Book of Judges, known for his leadership and his thirty sons who rode thirty donkeys and controlled thirty towns.
  • B. Marcos
    Marcos is a masculine given name, commonly used in Spanish- and Portuguese-speaking countries, that derives from the Latin name Marcus.
  • C. Floriano
    Floriano is a municipality in the Brazilian state of Piauí, known as an important commercial and cultural center in the region.
  • D. Mauricio
    Mauricio is a masculine given name, commonly used in Spanish- and Portuguese-speaking countries, derived from the Latin name Mauritius.
  • E. Augusto Vargas Alzamora
    Augusto Vargas Alzamora was a Peruvian Cardinal of the Roman Catholic Church who served as Archbishop of Lima and was known for his outspoken defense of human rights and democracy.
  • 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_69ca83d9844081908e561e367fda6d45 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cc9710ac04819096b9c8d3399b9c35 completed April 1, 2026, 3:54 a.m.
NED1 Entity disambiguation (via context triple) batch_69d01824ee2081909cc5e6ae33fab2e5 completed April 3, 2026, 7:42 p.m.
Created at: March 30, 2026, 7:15 p.m.