Triple

T15245657
Position Surface form Disambiguated ID Type / Status
Subject Rungis–La Fraternelle station E364372 entity
Predicate servedByLine P1293 FINISHED
Object RER C E13072 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 C | Statement: [Rungis–La Fraternelle station, servedByLine, RER C]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: RER C
Context triple: [Rungis–La Fraternelle station, servedByLine, RER C]
  • A. RER C chosen
    RER C is a major line of the Paris regional express rail network that connects central Paris with several suburbs and key destinations, including access to Orly Airport.
  • B. 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.
  • C. RER D
    RER D is one of the main lines of the Paris regional express network (RER), connecting northern and southern suburbs through central Paris.
  • D. RER Line C
    RER Line C is a major commuter rail line in the Paris RER network, running along the Seine and serving numerous suburbs and key destinations in the Île-de-France region.
  • E. 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.
  • 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_69d85a0dde7481908fc64d1e82d5d20d completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e007f306f08190be448b215d6c9b6c completed April 15, 2026, 9:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69fedd461cf08190a506aac2f0cec83a completed May 9, 2026, 7:07 a.m.
Created at: April 10, 2026, 3:13 a.m.