Triple

T10847378
Position Surface form Disambiguated ID Type / Status
Subject Paris–Rennes railway E256048 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: [Paris–Rennes railway, operator, SNCF]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: SNCF
Context triple: [Paris–Rennes railway, 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. SNCF Sud-Est region
    The SNCF Sud-Est region was a major operating division of the French national railway company responsible for managing and running rail services in the southeastern part of France, including key routes linking Paris with Lyon and the Mediterranean.
  • D. OUIGO
    OUIGO is a low-cost high-speed train service operated by France’s national railway company SNCF, offering budget-friendly travel on major routes.
  • E. Francorail
    Francorail was a French railway manufacturing consortium known for producing high-speed trainsets, including early models of the TGV.
  • 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_69d6aa81a5d08190aa86689061d1ddd2 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d75113bc188190ac78df0c51d95de6 completed April 9, 2026, 7:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69deb0c85d288190b13b1bc66921332c completed April 14, 2026, 9:25 p.m.
Created at: April 8, 2026, 9:20 p.m.