Triple

T5481349
Position Surface form Disambiguated ID Type / Status
Subject GSM core network E123472 entity
Predicate includesElement P11236 FINISHED
Object VLR
VLR (Visitor Location Register) is a key mobile network database that temporarily stores subscriber information and location details for users currently roaming within a specific area.
 E523115 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: VLR | Statement: [GSM core network, includesElement, VLR]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: VLR
Context triple: [GSM core network, includesElement, VLR]
  • A. VLR
    VLR is the abbreviation for the Virginia Landmarks Register, the Commonwealth of Virginia’s official list of historically significant properties and districts.
  • B. VL
    VL is the vehicle registration code used on license plates for vehicles registered in Râmnicu Vâlcea, Romania.
  • C. VLY
    VLY is the IATA airport code for Royal Air Force Valley, a military airbase on the island of Anglesey in Wales.
  • D. VLL
    VLL is the three-letter IATA airport code for Valladolid Airport in Spain.
  • E. VRA
    VRA is the common abbreviation for the landmark U.S. federal law enacted in 1965 to prohibit racial discrimination in voting.
  • 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: VLR
Triple: [GSM core network, includesElement, VLR]
Generated description
VLR (Visitor Location Register) is a key mobile network database that temporarily stores subscriber information and location details for users currently roaming within a specific area.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: VLR
Target entity description: VLR (Visitor Location Register) is a key mobile network database that temporarily stores subscriber information and location details for users currently roaming within a specific area.
  • A. VLR
    VLR is the abbreviation for the Virginia Landmarks Register, the Commonwealth of Virginia’s official list of historically significant properties and districts.
  • B. VL
    VL is the vehicle registration code used on license plates for vehicles registered in Râmnicu Vâlcea, Romania.
  • C. VLY
    VLY is the IATA airport code for Royal Air Force Valley, a military airbase on the island of Anglesey in Wales.
  • D. VLL
    VLL is the three-letter IATA airport code for Valladolid Airport in Spain.
  • E. VRA
    VRA is the common abbreviation for the landmark U.S. federal law enacted in 1965 to prohibit racial discrimination in voting.
  • 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_69bd4648883481909e9775d43300c5fa completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd924a2eb08190b759b23a6eab5e0a completed March 20, 2026, 6:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf48a2880c8190ad76cf8c3862aede completed March 22, 2026, 1:40 a.m.
NEDg Description generation batch_69bf4a95375881909ba730ad108eee8b completed March 22, 2026, 1:49 a.m.
NED2 Entity disambiguation (via description) batch_69bf4afb47a88190a66de6b6c7d5c241 completed March 22, 2026, 1:50 a.m.
Created at: March 20, 2026, 2:09 p.m.