Triple

T568071
Position Surface form Disambiguated ID Type / Status
Subject Siemens SD660 E13599 entity
Predicate builder P3143 FINISHED
Object Siemens E49800 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 | Statement: [Siemens SD660, builder, Siemens]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Siemens
Context triple: [Siemens SD660, builder, Siemens]
  • A. Siemens chosen
    Siemens is a major German multinational conglomerate best known for its leading roles in industrial manufacturing, energy, healthcare technology, and infrastructure solutions worldwide.
  • B. General Electric
    General Electric is a major American multinational conglomerate historically known for its leadership in industrial manufacturing, aviation, power, and healthcare technologies.
  • C. Rockwell Automation
    Rockwell Automation is a leading American industrial automation and information technology company known for providing control systems, software, and services to manufacturing and industrial clients worldwide.
  • D. Siemens SD660
    Siemens SD660 is a model of light rail vehicle built by Siemens for use in modern urban transit systems.
  • E. Thomson SA
    Thomson SA was a major French electronics and media conglomerate known for its consumer electronics, broadcasting, and defense-related technologies.
  • 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_69a4933fa4d88190a7949cc83c08c5c1 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49b02da148190b6a9bad3a22d8ec5 completed March 1, 2026, 8:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69a4fc81d6d4819090d91560efaa085d completed March 2, 2026, 2:57 a.m.
Created at: March 1, 2026, 7:33 p.m.