Triple

T17275552
Position Surface form Disambiguated ID Type / Status
Subject Siemens S200 E419381 entity
Predicate marketedAs P1395 FINISHED
Object S200
S200 is a Siemens-manufactured light rail vehicle model used by several transit agencies in North America for modern urban rail services.
E1260744 NE FINISHED

How this triple was built (4 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: S200 | Statement: [Siemens S200, marketedAs, S200]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: S200
Context triple: [Siemens S200, marketedAs, S200]
  • A. S200
    S200 is a pair of large solid rocket boosters used on India's GSLV Mk III heavy-lift launch vehicle to provide its initial thrust at liftoff.
  • B. S-200
    The S-200 is a long-range, Soviet-era surface-to-air missile system designed primarily to engage high-altitude aircraft and strategic targets.
  • C. S20
    S20 is a Chinese expressway route designation used for the S20 Outer Ring Expressway.
  • D. S-220
    S-220 is the hull number of NOAA Ship Fairweather, a National Oceanic and Atmospheric Administration vessel primarily used for hydrographic surveying and seafloor mapping.
  • E. R2000
    The R2000 is a 32-bit MIPS RISC microprocessor that became one of the earliest and most influential commercial implementations of the MIPS architecture in the mid-1980s.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: S200
Triple: [Siemens S200, marketedAs, S200]
Generated description
S200 is a Siemens-manufactured light rail vehicle model used by several transit agencies in North America for modern urban rail services.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: S200
Target entity description: S200 is a Siemens-manufactured light rail vehicle model used by several transit agencies in North America for modern urban rail services.
  • A. S200
    S200 is a pair of large solid rocket boosters used on India's GSLV Mk III heavy-lift launch vehicle to provide its initial thrust at liftoff.
  • B. S-200
    The S-200 is a long-range, Soviet-era surface-to-air missile system designed primarily to engage high-altitude aircraft and strategic targets.
  • C. S20
    S20 is a Chinese expressway route designation used for the S20 Outer Ring Expressway.
  • D. S-220
    S-220 is the hull number of NOAA Ship Fairweather, a National Oceanic and Atmospheric Administration vessel primarily used for hydrographic surveying and seafloor mapping.
  • E. R2000
    The R2000 is a 32-bit MIPS RISC microprocessor that became one of the earliest and most influential commercial implementations of the MIPS architecture in the mid-1980s.
  • F. None of above. chosen

Provenance (5 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_69d886da626481908a14ce7830329a35 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e4332625c48190a25bf51543d797c3 completed April 19, 2026, 1:43 a.m.
NED1 Entity disambiguation (via context triple) batch_6a01794f6cf481909d76e3d61f9888c5 completed May 11, 2026, 6:38 a.m.
NEDg Description generation batch_6a017b5abdc481909832a6e46d21b34b completed May 11, 2026, 6:46 a.m.
NED2 Entity disambiguation (via description) batch_6a017bcf8a6081908922f5ef2673abae completed May 11, 2026, 6:48 a.m.
Created at: April 10, 2026, 5:40 a.m.