Triple

T17261621
Position Surface form Disambiguated ID Type / Status
Subject Retiro district E419021 entity
Predicate borderedBy P224 FINISHED
Object Arganzuela district E533339 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: Arganzuela district | Statement: [Retiro district, borderedBy, Arganzuela district]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Arganzuela district
Context triple: [Retiro district, borderedBy, Arganzuela district]
  • A. Arganzuela district chosen
    Arganzuela district is a central district of Madrid, Spain, known for its mix of residential neighborhoods, cultural venues, and proximity to the Manzanares River.
  • B. Albayzín district
    The Albayzín district is Granada’s historic Moorish quarter, characterized by narrow winding streets, whitewashed houses, and iconic views of the Alhambra.
  • C. La Kalsa district
    La Kalsa district is a historic seaside quarter of Palermo, Sicily, known for its Arab-Norman heritage, narrow streets, and vibrant local culture.
  • D. Belén district
    Belén district is a riverside neighborhood in Iquitos, Peru, known for its stilt houses, floating structures, and bustling traditional market.
  • E. La Molina district
    La Molina district is an affluent residential and educational district located in the eastern part of Lima, Peru.
  • 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_69d886d9ab108190b70edd8d17aa1204 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e42e717a348190ae6835fb08f38125 completed April 19, 2026, 1:22 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0179445cac8190833eb7cd879a93bd completed May 11, 2026, 6:37 a.m.
Created at: April 10, 2026, 5:39 a.m.