Triple

T10754783
Position Surface form Disambiguated ID Type / Status
Subject Rouen tramway E253661 entity
Predicate hasLine P35 FINISHED
Object Line T2
Line T2 is one of the tram lines serving the city of Rouen, France, as part of its urban light rail network.
 E884440 NE FINISHED

How this triple was built (4 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 T2 | Statement: [Rouen tramway, hasLine, Line T2]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Line T2
Context triple: [Rouen tramway, hasLine, Line T2]
  • A. Line 2
    Line 2 is a major route of the Tunis Metro light rail network, serving key districts within the Tunis metropolitan area.
  • B. Line 2
    Line 2 is one of the two automated light metro lines of the Lille Metro system in northern France, serving numerous stations across the metropolitan area.
  • C. Line 2
    Line 2 is a planned second rapid transit line of the Turin Metro system in Turin, Italy, intended to expand the city's urban rail network.
  • D. Line 2
    Line 2 is a major rapid transit route of the STC Metro system, serving key districts along one of the network’s primary corridors.
  • E. Line 2
    Line 2 is a major rapid transit route of the Santo Domingo Metro system in the Dominican Republic, serving key east–west corridors of the capital.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Line T2
Triple: [Rouen tramway, hasLine, Line T2]
Generated description
Line T2 is one of the tram lines serving the city of Rouen, France, as part of its urban light rail network.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Line T2
Target entity description: Line T2 is one of the tram lines serving the city of Rouen, France, as part of its urban light rail network.
  • A. Line 2
    Line 2 is a major route of the Tunis Metro light rail network, serving key districts within the Tunis metropolitan area.
  • B. Line 2
    Line 2 is one of the two automated light metro lines of the Lille Metro system in northern France, serving numerous stations across the metropolitan area.
  • C. Line 2
    Line 2 is a planned second rapid transit line of the Turin Metro system in Turin, Italy, intended to expand the city's urban rail network.
  • D. Line 2
    Line 2 is a major rapid transit route of the STC Metro system, serving key districts along one of the network’s primary corridors.
  • E. Line 2
    Line 2 is a major rapid transit route of the Santo Domingo Metro system in the Dominican Republic, serving key east–west corridors of the capital.
  • F. None of above. chosen

Provenance (5 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_69d6aa5e51e8819095f06881cecf152e completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d72e9d0f688190a9be024929d2f960 completed April 9, 2026, 4:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69de2338b2cc8190ad40ff9a421a4152 completed April 14, 2026, 11:21 a.m.
NEDg Description generation batch_69de271ee56c81908d2f690f31c2d2db completed April 14, 2026, 11:38 a.m.
NED2 Entity disambiguation (via description) batch_69de2dff4a048190823c8b5f1f7ea548 completed April 14, 2026, 12:07 p.m.
Created at: April 8, 2026, 9:15 p.m.