Triple

T3831871
Position Surface form Disambiguated ID Type / Status
Subject MG E91030 entity
Predicate notableModel P1503 FINISHED
Object MG Metro
The MG Metro is a performance-oriented small hatchback produced by MG in the 1980s as a sportier, tuned version of the Austin/Rover Metro.
 E392653 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: MG Metro | Statement: [MG, notableModel, MG Metro]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MG Metro
Context triple: [MG, notableModel, MG Metro]
  • A. Metros
    Metros is the nickname historically used for the MetroStars, the former Major League Soccer team now known as the New York Red Bulls.
  • B. Metro
    "Metro" is a Russian disaster thriller film featuring Svetlana Khodchenkova in a prominent role, centered on a catastrophic flood in the Moscow subway system.
  • C. Metro
    Metro is the rapid transit system serving the Washington, D.C. metropolitan area, operated by the Washington Metropolitan Area Transit Authority (WMATA).
  • D. Metro
    Metro is the primary public transportation agency serving Los Angeles County, operating buses, light rail, subway, and other transit services across the region.
  • E. Metro
    Metro is a multinational wholesale and food retail company headquartered in Germany, operating cash-and-carry stores and serving professional customers worldwide.
  • 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: MG Metro
Triple: [MG, notableModel, MG Metro]
Generated description
The MG Metro is a performance-oriented small hatchback produced by MG in the 1980s as a sportier, tuned version of the Austin/Rover Metro.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MG Metro
Target entity description: The MG Metro is a performance-oriented small hatchback produced by MG in the 1980s as a sportier, tuned version of the Austin/Rover Metro.
  • A. Metros
    Metros is the nickname historically used for the MetroStars, the former Major League Soccer team now known as the New York Red Bulls.
  • B. Metro
    "Metro" is a Russian disaster thriller film featuring Svetlana Khodchenkova in a prominent role, centered on a catastrophic flood in the Moscow subway system.
  • C. Metro
    Metro is the rapid transit system serving the Washington, D.C. metropolitan area, operated by the Washington Metropolitan Area Transit Authority (WMATA).
  • D. Metro
    Metro is a multinational wholesale and food retail company headquartered in Germany, operating cash-and-carry stores and serving professional customers worldwide.
  • E. Metro
    Metro is the Los Angeles Police Department’s elite Metropolitan Division, known for handling specialized tactical operations, crowd control, and high-risk incidents.
  • 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_69aed960b538819096561c8ed448dec9 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeeb8787bc8190819a7af975b609df completed March 9, 2026, 3:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69b503fcddb481909690b708754d3d8a completed March 14, 2026, 6:45 a.m.
NEDg Description generation batch_69b5057089e88190b3c5b85503b3f8fa completed March 14, 2026, 6:51 a.m.
NED2 Entity disambiguation (via description) batch_69b505ec14b481909b71e66d68473c20 completed March 14, 2026, 6:53 a.m.
Created at: March 9, 2026, 3:17 p.m.