Triple

T13228987
Position Surface form Disambiguated ID Type / Status
Subject Bellas Artes station E314958 entity
Predicate servesLine P839 FINISHED
Object Line 5 E288960 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: Line 5 | Statement: [Bellas Artes station, servesLine, Line 5]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Line 5
Context triple: [Bellas Artes station, servesLine, Line 5]
  • A. Line 5
    Line 5 is a major north–south route of the Beijing Subway known for connecting key residential and commercial areas through the city center.
  • B. Line 5 chosen
    Line 5 is a major east–west route of the Brussels Metro system, connecting key districts across the Belgian capital.
  • C. Line 5
    Line 5 is one of the routes of the Tunis Metro light rail network, serving passengers across part of the Tunis metropolitan area.
  • D. Line 5
    Line 5 is a rapid transit line of the Shenzhen Metro system in Shenzhen, China, serving as one of the city's key urban rail corridors.
  • E. Line 5
    Line 5 is one of the main lines of the Paris Métro, running in a generally north–south direction and serving several key stations and neighborhoods across the city.
  • 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_69d806affc688190a25b6ccc588e9c72 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98d336ae08190bfc118cfbefddf84 completed April 10, 2026, 11:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69f70a3407388190bef886884cb75912 completed May 3, 2026, 8:41 a.m.
Created at: April 9, 2026, 9:21 p.m.