Triple

T9269952
Position Surface form Disambiguated ID Type / Status
Subject European Train Control System E222798 entity
Predicate hasComponent P35 FINISHED
Object GSM-R E273829 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: GSM-R | Statement: [European Train Control System, hasComponent, GSM-R]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: GSM-R
Context triple: [European Train Control System, hasComponent, GSM-R]
  • A. GSM-R chosen
    GSM-R is a digital radio communication system used across European railways to provide secure voice and data links between trains and railway control centers.
  • B. GSM
    GSM is the three-letter IATA airport code assigned to Qeshm International Airport in Iran.
  • C. GSM
    GSM is the common abbreviation for Great St Mary’s Church, the historic University Church located in the center of Cambridge, England.
  • D. GSM
    GSM is a second-generation (2G) digital mobile communication standard that became the global foundation for cellular voice and basic data services.
  • E. TETRA
    TETRA is a professional mobile radio and wireless communications standard widely used by public safety, emergency services, and critical infrastructure organizations for secure, reliable digital voice and data.
  • 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_69ca841ffe208190aa7bcffbef2f8379 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd078525308190abfb883123742d3f completed April 1, 2026, 11:54 a.m.
NED1 Entity disambiguation (via context triple) batch_69d09c2ca500819083ef4aa37f3e3a7f completed April 4, 2026, 5:05 a.m.
Created at: March 30, 2026, 7:33 p.m.