Triple

T20314297
Position Surface form Disambiguated ID Type / Status
Subject DB Cargo E510339 entity
Predicate formerName P65 FINISHED
Object DB Schenker Rail NE NERFINISHED

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: DB Schenker Rail | Statement: [DB Cargo, formerName, DB Schenker Rail]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: DB Schenker Rail
Context triple: [DB Cargo, formerName, DB Schenker Rail]
  • A. DB Schenker chosen
    DB Schenker is a global logistics and freight forwarding company providing land, air, and ocean transport as well as supply chain management services.
  • B. Rail Cargo Group
    Rail Cargo Group is the freight transport and logistics division of the Austrian Federal Railways (ÖBB), providing rail-based cargo services across Europe.
  • C. DB Cargo AG
    DB Cargo AG is the rail freight division of Deutsche Bahn, providing cargo and logistics services across Germany and Europe.
  • D. SBB Cargo
    SBB Cargo is the freight transport division of Switzerland’s national railway company, providing rail logistics and cargo services across Switzerland and into neighboring countries.
  • E. S7 Cargo
    S7 Cargo is the air freight and logistics division of Russia’s S7 Group, providing cargo transportation services on the airline’s route network.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4c7491c8190961113c4283b10b0 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e67745e2448190b5611382fe338bb2 completed April 20, 2026, 6:58 p.m.
Created at: April 16, 2026, 11:19 a.m.