Triple

T11083585
Position Surface form Disambiguated ID Type / Status
Subject Calle Génova E262061 entity
Predicate locatedIn P40 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: [Calle Génova, locatedIn, Zona Rosa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zona Rosa
Context triple: [Calle Génova, locatedIn, 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_69d6aa9983c08190b0ef61603b69feac completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d799bf89f48190889f08d2f5dd220a completed April 9, 2026, 12:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3e79854c88190bda69cfbe4ae9d1e completed April 18, 2026, 8:20 p.m.
Created at: April 8, 2026, 9:27 p.m.