Triple

T1791954
Position Surface form Disambiguated ID Type / Status
Subject Siemens Charger E39515 entity
Predicate familyIncludes P3600 FINISHED
Object Siemens SC-44 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 SC-44 | Statement: [Siemens Charger, familyIncludes, Siemens SC-44]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Siemens SC-44
Context triple: [Siemens Charger, familyIncludes, Siemens SC-44]
  • A. Siemens SD660
    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 chosen
    The Siemens Charger is a family of modern diesel-electric passenger locomotives widely used across North America for intercity and commuter rail services.
  • D. Honeywell 316 minicomputer
    The Honeywell 316 minicomputer was a small, 16-bit general-purpose computer from the late 1960s widely used in early networking and control applications.
  • E. SINTRAN
    SINTRAN is a real-time, multitasking operating system developed by Norsk Data for its NORD series of minicomputers.
  • 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_69a88631854081909723959921e45c2b completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa65392b2c81909bf4d619bd347f54 completed March 6, 2026, 5:25 a.m.
NED1 Entity disambiguation (via context triple) batch_69adb5d063d48190aef6796ee3957994 completed March 8, 2026, 5:45 p.m.
Created at: March 4, 2026, 7:32 p.m.