Triple

T563402
Position Surface form Disambiguated ID Type / Status
Subject Amtrak Capitol Corridor E13501 entity
Predicate usesLocomotive P5426 FINISHED
Object Siemens Charger E39515 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: Siemens Charger | Statement: [Amtrak Capitol Corridor, usesLocomotive, Siemens Charger]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Siemens Charger
Context triple: [Amtrak Capitol Corridor, usesLocomotive, Siemens Charger]
  • A. Siemens Charger chosen
    The Siemens Charger is a family of modern diesel-electric passenger locomotives widely used across North America for intercity and commuter rail services.
  • B. Siemens SD660
    Siemens SD660 is a model of light rail vehicle built by Siemens for use in modern urban transit systems.
  • C. Siemens ACS-64
    The Siemens ACS-64 is a high-speed, electric locomotive used by Amtrak for passenger rail service in the United States.
  • D. Siemens S70
    The Siemens S70 is a modern low-floor light rail vehicle widely used in North American urban transit systems.
  • E. Siemens S700 light rail vehicles
    Siemens S700 light rail vehicles are modern low-floor light rail cars designed by Siemens Mobility for urban transit systems, featuring improved accessibility, energy efficiency, and passenger comfort.
  • 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_69a4933edcf08190b35ecfd6014caee6 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49a712bc48190ba298b3c76ab11cc completed March 1, 2026, 7:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69a4ed37a98081909afbc0de4079dda8 completed March 2, 2026, 1:51 a.m.
Created at: March 1, 2026, 7:32 p.m.