Triple

T6724116
Position Surface form Disambiguated ID Type / Status
Subject RER line E E153469 entity
Predicate terminus P388 FINISHED
Object Chelles–Gournay
Chelles–Gournay is a suburban railway station in the eastern Paris metropolitan area that serves as the outer terminus of RER line E.
 E751497 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: Chelles–Gournay | Statement: [RER line E, terminus, Chelles–Gournay]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Chelles–Gournay
Context triple: [RER line E, terminus, Chelles–Gournay]
  • A. Crosne
    Crosne is a small suburban commune in the Île-de-France region of northern France, located southeast of Paris.
  • B. Gonesse
    Gonesse is a commune in the northeastern suburbs of Paris, France, known historically as a rural town and now as part of the greater Paris metropolitan area.
  • C. Vaucresson
    Vaucresson is a suburban commune in the western outskirts of Paris, France, known for its residential character and green surroundings.
  • D. Bourgueil
    Bourgueil is a Loire Valley wine appellation in France renowned for its red wines, particularly those made predominantly from Cabernet Franc.
  • E. Grigny
    Grigny is a suburban commune in the southern outskirts of Paris, France, known for its large housing estates and diverse population.
  • 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: Chelles–Gournay
Triple: [RER line E, terminus, Chelles–Gournay]
Generated description
Chelles–Gournay is a suburban railway station in the eastern Paris metropolitan area that serves as the outer terminus of RER line E.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Chelles–Gournay
Target entity description: Chelles–Gournay is a suburban railway station in the eastern Paris metropolitan area that serves as the outer terminus of RER line E.
  • A. Crosne
    Crosne is a small suburban commune in the Île-de-France region of northern France, located southeast of Paris.
  • B. Gonesse
    Gonesse is a commune in the northeastern suburbs of Paris, France, known historically as a rural town and now as part of the greater Paris metropolitan area.
  • C. Vaucresson
    Vaucresson is a suburban commune in the western outskirts of Paris, France, known for its residential character and green surroundings.
  • D. Bourgueil
    Bourgueil is a Loire Valley wine appellation in France renowned for its red wines, particularly those made predominantly from Cabernet Franc.
  • E. Grigny
    Grigny is a suburban commune in the southern outskirts of Paris, France, known for its large housing estates and diverse population.
  • 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_69c6880afb988190ad88011b48ecfcba completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d13b296c8190bf54009063032c6d completed March 27, 2026, 6:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69cef26fd12c819082f644bc727584ec completed April 2, 2026, 10:49 p.m.
NEDg Description generation batch_69cef4cbc0788190bf95aa8058a8d895 completed April 2, 2026, 10:59 p.m.
NED2 Entity disambiguation (via description) batch_69cef830e4e48190bf337a526cc82119 completed April 2, 2026, 11:13 p.m.
Created at: March 27, 2026, 2:08 p.m.