Triple

T6262889
Position Surface form Disambiguated ID Type / Status
Subject Junín Region E140342 entity
Predicate contains P35 FINISHED
Object Junín city E140342 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: Junín city | Statement: [Junín Region, contains, Junín city]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Junín city
Context triple: [Junín Region, contains, Junín city]
  • A. Junín chosen
    Junín is a central highland region of Peru known for its Andean landscapes, rich mining and agricultural activities, and historical role in Peru’s independence.
  • B. Junín
    Junín is a major oil-producing area within Venezuela’s Orinoco Belt, known for its vast extra-heavy crude reserves.
  • C. Bahía Blanca
    Bahía Blanca is a major port city in southern Buenos Aires Province, Argentina, known for its industrial activity and strategic location on the Atlantic coast.
  • D. San Miguel de Tucumán
    San Miguel de Tucumán is a historic city in northwest Argentina known as the birthplace of the country’s independence, where the 1816 declaration was signed.
  • E. Gualeguaychú
    Gualeguaychú is a city in eastern Argentina known for its vibrant Carnival celebrations and riverside tourism.
  • 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_69c008c95c5c819084bd3dd56133d84d completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c06387fec0819095b47a37b9402aa9 completed March 22, 2026, 9:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6caf09b408190b1133afa52668bfe completed March 27, 2026, 6:22 p.m.
Created at: March 22, 2026, 4:25 p.m.