Triple

T427806
Position Surface form Disambiguated ID Type / Status
Subject Chemnitz Hauptbahnhof E9646 entity
Predicate fareZone P844 FINISHED
Object VMS
VMS is a regional public transport association in the Chemnitz area of Germany that coordinates and manages integrated fares and services across multiple transit operators.
 E54322 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: VMS | Statement: [Chemnitz Hauptbahnhof, fareZone, VMS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: VMS
Context triple: [Chemnitz Hauptbahnhof, fareZone, VMS]
  • A. Tymshare
    Tymshare was an influential American time-sharing and computer services company active in the 1960s–1980s that helped pioneer remote computing and software services for businesses.
  • B. DOS
    DOS is the commonly used acronym for the United States Department of State, the federal executive department responsible for U.S. foreign policy and international relations.
  • C. VZ
    VZ is the stock ticker symbol for Verizon Communications Inc., a major U.S.-based telecommunications company providing wireless, internet, and related services.
  • D. VNM
    VNM is the three-letter ISO 3166-1 alpha-3 country code assigned to Vietnam.
  • E. VMX
    VMX is a vector processing extension to the PowerPC architecture designed to accelerate multimedia, signal processing, and other parallelizable computations.
  • 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: VMS
Triple: [Chemnitz Hauptbahnhof, fareZone, VMS]
Generated description
VMS is a regional public transport association in the Chemnitz area of Germany that coordinates and manages integrated fares and services across multiple transit operators.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: VMS
Target entity description: VMS is a regional public transport association in the Chemnitz area of Germany that coordinates and manages integrated fares and services across multiple transit operators.
  • A. Tymshare
    Tymshare was an influential American time-sharing and computer services company active in the 1960s–1980s that helped pioneer remote computing and software services for businesses.
  • B. DOS
    DOS is the commonly used acronym for the United States Department of State, the federal executive department responsible for U.S. foreign policy and international relations.
  • C. VZ
    VZ is the stock ticker symbol for Verizon Communications Inc., a major U.S.-based telecommunications company providing wireless, internet, and related services.
  • D. VNM
    VNM is the three-letter ISO 3166-1 alpha-3 country code assigned to Vietnam.
  • E. VMX
    VMX is a vector processing extension to the PowerPC architecture designed to accelerate multimedia, signal processing, and other parallelizable computations.
  • 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_69a2e801e1d48190b505d1dd336b52ac completed Feb. 28, 2026, 1:05 p.m.
NER Named-entity recognition batch_69a2eed7f3508190995dcd39586ed614 completed Feb. 28, 2026, 1:34 p.m.
NED1 Entity disambiguation (via context triple) batch_69a42f665c2881908850bce36cdf74b8 completed March 1, 2026, 12:21 p.m.
NEDg Description generation batch_69a43038d2348190a348e6661d27dde4 completed March 1, 2026, 12:25 p.m.
NED2 Entity disambiguation (via description) batch_69a430f6c6f88190b5aecfe3c4c8957d completed March 1, 2026, 12:28 p.m.
Created at: Feb. 28, 2026, 1:11 p.m.