Triple

T2391133
Position Surface form Disambiguated ID Type / Status
Subject Metro Line 6 E48944 entity
Predicate hasInterchangeStation P2413 FINISHED
Object El Rosario E46955 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: El Rosario | Statement: [Metro Line 6, hasInterchangeStation, El Rosario]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: El Rosario
Context triple: [Metro Line 6, hasInterchangeStation, El Rosario]
  • A. El Rosario chosen
    El Rosario is a major Mexico City transit hub and neighborhood that serves as a key terminus and interchange point for multiple public transportation lines.
  • B. El Rosario
    El Rosario is a municipality on the island of Tenerife in Spain’s Canary Islands, known for its coastal landscapes and proximity to the island’s capital, Santa Cruz de Tenerife.
  • C. De Rosario
    De Rosario is the surname of Dwayne De Rosario, a prominent Canadian former professional soccer player known for his goal-scoring and playmaking in Major League Soccer.
  • D. Rosario
    Rosario is a major Argentine port city and industrial center located in the province of Santa Fe.
  • E. Rosario
    Rosario is a coastal municipality in the Mexican state of Sinaloa known for its historic architecture, mining heritage, and proximity to the Pacific Ocean.
  • 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_69a88aa5f63081908d07fd302029fcbd completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abc87457388190822d5506327db8f2 completed March 7, 2026, 6:40 a.m.
NED1 Entity disambiguation (via context triple) batch_69aef09854fc8190bab0415e3815a920 completed March 9, 2026, 4:08 p.m.
Created at: March 4, 2026, 7:57 p.m.