Triple

T9398977
Position Surface form Disambiguated ID Type / Status
Subject Colonia Cuauhtémoc E226418 entity
Predicate near P350 FINISHED
Object Zona Rosa E44382 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: Zona Rosa | Statement: [Colonia Cuauhtémoc, near, Zona Rosa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zona Rosa
Context triple: [Colonia Cuauhtémoc, near, Zona Rosa]
  • A. Zona Rosa chosen
    Zona Rosa is a lively commercial and nightlife district in Mexico City known for its shopping, restaurants, bars, and LGBTQ+ scene.
  • B. Luz district
    Luz district is a historic central neighborhood in São Paulo, Brazil, known for its major cultural institutions, transport hub, and architectural landmarks.
  • C. Vedado
    Vedado is a prominent residential and cultural neighborhood in Havana, Cuba, known for its modernist architecture, nightlife, and proximity to the Malecón waterfront.
  • D. Barrio del Carmen
    Barrio del Carmen is a historic and lively neighborhood in Valencia, Spain, known for its medieval streets, vibrant nightlife, and rich cultural heritage.
  • E. Ciudad Lineal district
    Ciudad Lineal is a largely residential district in the eastern part of Madrid, Spain, known for its linear urban layout, diverse neighborhoods, and strong public transport connections.
  • 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_69ca843170f88190800a8ab2b5fc568e completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd51556fc08190b8ff8190a1485a3a completed April 1, 2026, 5:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1221da00c8190ada494660735f741 completed April 4, 2026, 2:37 p.m.
Created at: March 30, 2026, 7:46 p.m.