Triple

T1035588
Position Surface form Disambiguated ID Type / Status
Subject MAX Orange Line E22353 entity
Predicate usesRollingStock P5426 FINISHED
Object Siemens SD660 E13599 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 SD660 | Statement: [MAX Orange Line, usesRollingStock, Siemens SD660]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Siemens SD660
Context triple: [MAX Orange Line, usesRollingStock, Siemens SD660]
  • A. Siemens SD660 chosen
    Siemens SD660 is a model of light rail vehicle built by Siemens for use in modern urban transit systems.
  • B. Siemens S70
    The Siemens S70 is a modern low-floor light rail vehicle widely used in North American urban transit systems.
  • C. Siemens Charger
    The Siemens Charger is a family of modern diesel-electric passenger locomotives widely used across North America for intercity and commuter rail services.
  • D. Siemens
    Siemens is a major German multinational conglomerate best known for its leading roles in industrial manufacturing, energy, healthcare technology, and infrastructure solutions worldwide.
  • E. Siemens ACS-64
    The Siemens ACS-64 is a high-speed, electric locomotive used by Amtrak for passenger rail service in the United States.
  • 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_69a493d848848190aed4011b34b2e8d3 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b816272c8190a12e470c4d4ebcf9 completed March 1, 2026, 10:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac3bc378fc8190846d5ffce73371dd completed March 7, 2026, 2:52 p.m.
Created at: March 1, 2026, 7:41 p.m.