Triple

T10541127
Position Surface form Disambiguated ID Type / Status
Subject TGV Ouigo E248696 entity
Predicate notableRoute P22 FINISHED
Object Paris–Rennes
Paris–Rennes is a major high-speed rail corridor in France linking the capital Paris with the city of Rennes in Brittany.
 E872234 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: Paris–Rennes | Statement: [TGV Ouigo, notableRoute, Paris–Rennes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Paris–Rennes
Context triple: [TGV Ouigo, notableRoute, Paris–Rennes]
  • A. Paris–Bordeaux
    Paris–Bordeaux is a major high-speed rail corridor in France connecting the capital with the southwest, known for its fast TGV services.
  • B. Paris–Clermont-Ferrand
    Paris–Clermont-Ferrand is a major French intercity rail route linking the capital Paris with the central city of Clermont-Ferrand.
  • C. Paris–Brest
    Paris–Brest is a long-distance French railway service connecting Paris with the city of Brest in Brittany.
  • D. Paris–Toulouse
    Paris–Toulouse is a major intercity rail corridor in France linking the capital Paris with the southwestern city of Toulouse.
  • E. Paris–Côte d’Azur
    Paris–Côte d’Azur was a prestigious French express train service linking Paris with the French Riviera, renowned for its luxury and popularity among holiday travelers.
  • 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: Paris–Rennes
Triple: [TGV Ouigo, notableRoute, Paris–Rennes]
Generated description
Paris–Rennes is a major high-speed rail corridor in France linking the capital Paris with the city of Rennes in Brittany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Paris–Rennes
Target entity description: Paris–Rennes is a major high-speed rail corridor in France linking the capital Paris with the city of Rennes in Brittany.
  • A. Paris–Bordeaux
    Paris–Bordeaux is a major high-speed rail corridor in France connecting the capital with the southwest, known for its fast TGV services.
  • B. Paris–Clermont-Ferrand
    Paris–Clermont-Ferrand is a major French intercity rail route linking the capital Paris with the central city of Clermont-Ferrand.
  • C. Paris–Brest
    Paris–Brest is a long-distance French railway service connecting Paris with the city of Brest in Brittany.
  • D. Paris–Toulouse
    Paris–Toulouse is a major intercity rail corridor in France linking the capital Paris with the southwestern city of Toulouse.
  • E. Paris–Côte d’Azur
    Paris–Côte d’Azur was a prestigious French express train service linking Paris with the French Riviera, renowned for its luxury and popularity among holiday travelers.
  • 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_69d381c733c08190ab1dd6239f5f34ae completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d50a5918648190b16c2d1bc1bf015f completed April 7, 2026, 1:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69d94b23b2988190b536d5ecb76298ff completed April 10, 2026, 7:10 p.m.
NEDg Description generation batch_69d94ca07da481908f2d546f8ddc9326 completed April 10, 2026, 7:16 p.m.
NED2 Entity disambiguation (via description) batch_69d94e8687bc819082b672a64bf85500 completed April 10, 2026, 7:24 p.m.
Created at: April 6, 2026, 12:32 p.m.