Triple

T10754784
Position Surface form Disambiguated ID Type / Status
Subject Rouen tramway E253661 entity
Predicate hasLine P35 FINISHED
Object Line T3
Line T3 is a route of the Rouen tramway network in France, providing urban light-rail transit service within the Rouen metropolitan area.
E885295 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 T3 | Statement: [Rouen tramway, hasLine, Line T3]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Line T3
Context triple: [Rouen tramway, hasLine, Line T3]
  • A. Line T2
    Line T2 is one of the tram lines serving the city of Rouen, France, as part of its urban light rail network.
  • B. Line 3
    Line 3 is a major rapid transit route of the Guangzhou Metro system, known for its high passenger volume and key role in connecting central urban areas with the airport and suburban districts.
  • C. Line 3
    Line 3 is a major north–south route of the Seoul Metropolitan Subway system, connecting key residential and commercial districts across the city and into surrounding areas.
  • D. Line 3
    Line 3 is a future rapid transit route of the Seville Metro intended to extend and improve the city’s urban rail network.
  • E. Line 3
    Line 3 is a major line of the Saint Petersburg Metro system, serving as one of the city's primary rapid transit routes.
  • 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 T3
Triple: [Rouen tramway, hasLine, Line T3]
Generated description
Line T3 is a route of the Rouen tramway network in France, providing urban light-rail transit service within the Rouen metropolitan area.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Line T3
Target entity description: Line T3 is a route of the Rouen tramway network in France, providing urban light-rail transit service within the Rouen metropolitan area.
  • A. Line T2
    Line T2 is one of the tram lines serving the city of Rouen, France, as part of its urban light rail network.
  • B. Line 3
    Line 3 is a major rapid transit route of the Guangzhou Metro system, known for its high passenger volume and key role in connecting central urban areas with the airport and suburban districts.
  • C. Line 3
    Line 3 is a major rapid transit line of the Chongqing Metro system in Chongqing, China, known for its extensive elevated monorail route that connects key urban and suburban areas.
  • D. Line 3
    Line 3 is a major north–south route of the Seoul Metropolitan Subway system, connecting key residential and commercial districts across the city and into surrounding areas.
  • E. Line 3
    Line 3 is a major line of the Saint Petersburg Metro system, serving as one of the city's primary rapid transit routes.
  • 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_69de55a12b8c8190bbeeb6d176f42b49 completed April 14, 2026, 2:56 p.m.
NEDg Description generation batch_69de5952f6c48190abd3b87372d54f58 completed April 14, 2026, 3:12 p.m.
NED2 Entity disambiguation (via description) batch_69de5ed49c9c8190a4085407f88d7a05 completed April 14, 2026, 3:35 p.m.
Created at: April 8, 2026, 9:15 p.m.