Triple

T17072194
Position Surface form Disambiguated ID Type / Status
Subject Versailles-Rive-Gauche–Château de Versailles E414249 entity
Predicate railNetwork P522 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: [Versailles-Rive-Gauche–Château de Versailles, railNetwork, RER C]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: RER C
Context triple: [Versailles-Rive-Gauche–Château de Versailles, railNetwork, 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_69d886cef44c8190ba56c44b4e863e64 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3dbc1b7d48190979a848b4188cb22 completed April 18, 2026, 7:30 p.m.
NED1 Entity disambiguation (via context triple) batch_6a012edbce988190a784448ba8a258a5 completed May 11, 2026, 1:20 a.m.
Created at: April 10, 2026, 5:34 a.m.