Triple

T11800829
Position Surface form Disambiguated ID Type / Status
Subject San Nicolás E280620 entity
Predicate partOf P40 FINISHED
Object Comuna 1 E733121 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: Comuna 1 | Statement: [San Nicolás, partOf, Comuna 1]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Comuna 1
Context triple: [San Nicolás, partOf, Comuna 1]
  • A. Comuna 3
    Comuna 3 is an administrative division of Buenos Aires, Argentina, that encompasses central neighborhoods including Balvanera.
  • B. Comuna 4
    Comuna 4 is an administrative commune of Buenos Aires that includes neighborhoods such as La Boca in the city’s southern area.
  • C. Comuna 1 of Buenos Aires chosen
    Comuna 1 of Buenos Aires is a central administrative district of Argentina’s capital city that encompasses several historic and downtown neighborhoods, including Monserrat.
  • D. Comuna 15 of Buenos Aires
    Comuna 15 of Buenos Aires is an administrative district in the northern part of Argentina’s capital city, encompassing several residential neighborhoods with a mix of commercial areas and urban infrastructure.
  • E. María Elena Commune
    María Elena Commune is an administrative division in Chile’s Antofagasta Region, historically known for its nitrate mining industry and desert landscape.
  • 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_69d6ab258b808190b1735835c841e3a4 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8a5a4512c8190b7782e1dee053000 completed April 10, 2026, 7:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69f13129fa608190b080dc27f8bd7803 completed April 28, 2026, 10:14 p.m.
Created at: April 8, 2026, 9:42 p.m.