Triple

T6838705
Position Surface form Disambiguated ID Type / Status
Subject Avignon TGV station E157515 entity
Predicate operator P179 FINISHED
Object SNCF E37919 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: SNCF | Statement: [Avignon TGV station, operator, SNCF]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: SNCF
Context triple: [Avignon TGV station, operator, SNCF]
  • A. SNCF chosen
    SNCF is France’s national state-owned railway company, responsible for operating the country’s passenger and freight rail services and much of its rail infrastructure.
  • B. SNCF Réseau
    SNCF Réseau is the French state-owned rail infrastructure manager responsible for operating, maintaining, and developing France’s national railway network.
  • C. Francorail
    Francorail was a French railway manufacturing consortium known for producing high-speed trainsets, including early models of the TGV.
  • D. SNCF Connect
    SNCF Connect is the official digital platform and app of the French national railway company, providing online ticket booking, travel planning, and real-time information for trains and other transport services.
  • E. SNCF Voyageurs
    SNCF Voyageurs is the passenger rail operating division of France’s national railway company, responsible for running high-speed, regional, and commuter train services.
  • 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_69c6882c53608190b99aebef079b23bd completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d67ee1c88190b82a9b6b3d1e3875 completed March 27, 2026, 7:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7240321108190860e91ebeb738a8f completed March 28, 2026, 12:42 a.m.
Created at: March 27, 2026, 2:19 p.m.