Triple

T9732420
Position Surface form Disambiguated ID Type / Status
Subject Central Paris E235976 entity
Predicate transportHubFor P423 FINISHED
Object RER network E68667 NE FINISHED

How this triple was built (2 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: RER network | Statement: [Central Paris, transportHubFor, RER network]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: RER network
Context triple: [Central Paris, transportHubFor, RER network]
  • A. RER network chosen
    The RER network is a rapid transit system of express suburban trains serving Paris and its surrounding metropolitan area, integrating both urban and regional rail services.
  • B. RER A
    RER A is one of the main lines of the Paris regional express network, carrying large volumes of commuters and travelers between central Paris and its suburbs.
  • C. RER E
    RER E is a line of the Paris express suburban rail network (Réseau Express Régional) serving eastern suburbs and connecting them to central Paris.
  • D. RER B line
    The RER B line is a major Paris regional express railway line that connects central Paris with key northern and southern suburbs, including Charles de Gaulle Airport.
  • E. RER NG
    RER NG is a new-generation double-deck electric multiple unit train designed for Île-de-France’s RER network, offering higher capacity, improved accessibility, and enhanced passenger comfort.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69ca84d313e88190983ee6ffd0ef60d2 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9eb3d6e4819090b3c7fb92550c57 completed April 1, 2026, 10:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1d59e0c8c8190888b56d75f9ba2c2 completed April 5, 2026, 3:23 a.m.
Created at: March 30, 2026, 8:22 p.m.