Triple

T17778293
Position Surface form Disambiguated ID Type / Status
Subject Canal de San Juan station E443830 entity
Predicate locatedOnLine P2409 FINISHED
Object Line A — NE NERFINISHED

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: Line A | Statement: [Canal de San Juan station, locatedOnLine, Line A]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Line A
Context triple: [Canal de San Juan station, locatedOnLine, Line A]
  • A. Line A chosen
    Line A is the historic first subway line of the Buenos Aires Underground, known for its early 20th-century wooden cars and route through central neighborhoods.
  • B. Line A
    Line A is one of the main tram lines serving the city of Reims, France, providing urban public transportation across key districts.
  • C. Line A
    Line A is one of the main routes of the Strasbourg tramway network, providing key light-rail transit across the city.
  • D. Line A
    Line A is one of the main lines of the Prague Metro, running east–west through the city and serving several central and residential districts.
  • E. Line A
    Line A is one of the main routes of the Porto Metro light rail system in Porto, Portugal, connecting key urban and suburban areas.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b9ef17708190bdf7e2adbf14ddc2 completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e4871f63708190b298ed96896ad0ee completed April 19, 2026, 7:41 a.m.
Created at: April 10, 2026, 10:12 a.m.