Triple

T14872436
Position Surface form Disambiguated ID Type / Status
Subject Jacques de Liniers E349780 entity
Predicate nobleTitle P914 FINISHED
Object Count of Buenos Aires E349781 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: Count of Buenos Aires | Statement: [Jacques de Liniers, nobleTitle, Count of Buenos Aires]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Count of Buenos Aires
Context triple: [Jacques de Liniers, nobleTitle, Count of Buenos Aires]
  • A. Count of Buenos Aires chosen
    Count of Buenos Aires is a Spanish noble title historically associated with Santiago de Liniers, a key colonial-era figure in the defense and governance of Buenos Aires.
  • B. Campana, Buenos Aires
    Campana is an industrial port city in the Buenos Aires Province of Argentina, located on the Paraná River northwest of Buenos Aires city.
  • C. 42 Buenos Aires
    42 Buenos Aires is an Argentine campus of the global, tuition-free 42 coding school network, offering peer-to-peer, project-based programming education.
  • D. Colonia Buenos Aires
    Colonia Buenos Aires is a neighborhood located within the Cuauhtémoc borough in central Mexico City.
  • E. Buenos Aires
    Buenos Aires is the capital and largest city of Argentina, known for its rich European-influenced culture, tango music and dance, and vibrant urban life.
  • 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_69d822ee4f408190b6ac3b2fa434f0df completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69ded5e2c94c8190a16f05ea81701fc1 completed April 15, 2026, 12:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe72aad76c8190b024651483d8f9ff completed May 8, 2026, 11:32 p.m.
Created at: April 10, 2026, 1:55 a.m.