Triple

T3997984
Position Surface form Disambiguated ID Type / Status
Subject Paris–Lille E87143 entity
Predicate servedBy P82 FINISHED
Object TGV
TGV is France’s high-speed intercity train service, renowned for rapid connections between major cities such as Paris and Lille.
 E445505 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: TGV | Statement: [Paris–Lille, servedBy, TGV]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TGV
Context triple: [Paris–Lille, servedBy, TGV]
  • A. TGV Est
    TGV Est is a French high-speed train service connecting Paris with eastern France and neighboring European countries such as Germany, Luxembourg, and Switzerland.
  • B. TGV Ouigo
    TGV Ouigo is a low-cost high-speed train service operated by SNCF in France, offering budget fares on selected TGV routes.
  • C. TGV PSE
    TGV PSE is the original generation of French high-speed TGV Sud-Est trainsets that inaugurated high-speed rail service in France.
  • D. TGV Réseau
    TGV Réseau is a later-generation French high-speed trainset used by SNCF, designed for improved performance and comfort on the expanding TGV network.
  • E. Bezannes TGV
    Bezannes TGV is a tram terminus and transport hub in the suburb of Bezannes serving the high-speed TGV rail connections near Reims, France.
  • 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: TGV
Triple: [Paris–Lille, servedBy, TGV]
Generated description
TGV is France’s high-speed intercity train service, renowned for rapid connections between major cities such as Paris and Lille.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: TGV
Target entity description: TGV is France’s high-speed intercity train service, renowned for rapid connections between major cities such as Paris and Lille.
  • A. TGV Est
    TGV Est is a French high-speed train service connecting Paris with eastern France and neighboring European countries such as Germany, Luxembourg, and Switzerland.
  • B. TGV Ouigo
    TGV Ouigo is a low-cost high-speed train service operated by SNCF in France, offering budget fares on selected TGV routes.
  • C. TGV PSE
    TGV PSE is the original generation of French high-speed TGV Sud-Est trainsets that inaugurated high-speed rail service in France.
  • D. TGV Réseau
    TGV Réseau is a later-generation French high-speed trainset used by SNCF, designed for improved performance and comfort on the expanding TGV network.
  • E. Bezannes TGV
    Bezannes TGV is a tram terminus and transport hub in the suburb of Bezannes serving the high-speed TGV rail connections near Reims, France.
  • 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_69aed94118148190975e6aa4e554cde9 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefa3ef7ac8190abe02f440ff83c43 completed March 9, 2026, 4:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69badb16faf88190aa047ba701ff7c0d completed March 18, 2026, 5:04 p.m.
NEDg Description generation batch_69bb14ba963481908593c3c140e500e3 completed March 18, 2026, 9:10 p.m.
NED2 Entity disambiguation (via description) batch_69bb1522097c8190bbe0b5f5216e76af completed March 18, 2026, 9:12 p.m.
Created at: March 9, 2026, 3:34 p.m.