Triple

T16089550
Position Surface form Disambiguated ID Type / Status
Subject Muzeum E390324 entity
Predicate servesLine P839 FINISHED
Object Line C E390323 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 C | Statement: [Muzeum, servesLine, Line C]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Line C
Context triple: [Muzeum, servesLine, Line C]
  • A. Line C
    Line C is one of the main lines of the Buenos Aires Underground, running north–south through central Buenos Aires and connecting key transport hubs in the city.
  • B. Line C
    Line C is one of the routes of the Strasbourg tramway network, providing urban light-rail transit service within the city and its suburbs.
  • C. Line C chosen
    Line C is one of the main lines of the Prague Metro, running in a north–south direction and serving as a key backbone of the city’s rapid transit network.
  • D. Line C
    Line C is one of the routes of the Porto Metro light rail network in Porto, Portugal, serving suburban and urban areas along its corridor.
  • E. Line C
    Line C is one of the main routes of the Rotterdam Metro rapid transit system in the Netherlands, connecting key districts across the metropolitan area.
  • 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_69d87f198bc48190a8b7e53ca15b7ead completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e1845161908190adca2af94710b2cc completed April 17, 2026, 12:52 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffe490d494819081f812811f032702 completed May 10, 2026, 1:51 a.m.
Created at: April 10, 2026, 4:59 a.m.