Triple

T18936133
Position Surface form Disambiguated ID Type / Status
Subject Siemens SD100 E463250 entity
Predicate hasSuccessor P78 FINISHED
Object Siemens SD160 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: Siemens SD160 | Statement: [Siemens SD100, hasSuccessor, Siemens SD160]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Siemens SD160
Context triple: [Siemens SD100, hasSuccessor, Siemens SD160]
  • A. Siemens SD-160 chosen
    The Siemens SD-160 is a high-floor light rail vehicle widely used in North American transit systems, including Calgary’s CTrain, known for its modular design and reliable urban service.
  • B. Siemens SD100
    The Siemens SD100 is a light rail vehicle model built by Siemens for use on urban trolley and light rail systems such as the San Diego Trolley.
  • C. Siemens SD-460
    The Siemens SD-460 is a light rail vehicle model built by Siemens for use on systems such as the St. Louis MetroLink.
  • D. Siemens SD660
    Siemens SD660 is a model of light rail vehicle built by Siemens for use in modern urban transit systems.
  • E. Siemens SD-400
    The Siemens SD-400 is a light rail vehicle model built by Siemens for use on systems such as the St. Louis MetroLink, featuring articulated, electrically powered cars designed for urban transit.
  • 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_69d8dcfec90481909e926be9767e5779 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5d3e7b87c81909dc29defb33c8e00 completed April 20, 2026, 7:21 a.m.
Created at: April 10, 2026, 11:59 a.m.