Triple

T3782449
Position Surface form Disambiguated ID Type / Status
Subject Montreal Metro E85450 entity
Predicate rollingStock P1305 FINISHED
Object MPM-10
MPM-10 is a modern rubber-tired metro train model used on the Montreal Metro, designed to increase capacity, comfort, and energy efficiency.
 E387553 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: MPM-10 | Statement: [Montreal Metro, rollingStock, MPM-10]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MPM-10
Context triple: [Montreal Metro, rollingStock, MPM-10]
  • A. MP 05
    MP 05 is a rubber-tyred, fully automated train model used on the Paris Métro, notably on Line 1 and Line 14.
  • B. MP 43
    MP 43 is an early German World War II assault rifle prototype that evolved into the famous Sturmgewehr 44, one of the first true assault rifles in history.
  • C. MP 14
    MP 14 is a modern rubber-tyred train model used on the Paris Métro, designed for improved energy efficiency, automation, and passenger comfort.
  • D. MPS
    MPS is a leading German research institute specializing in the study of the Sun and the solar system, operating under the Max Planck Society.
  • E. MPS
    MPS is a language workbench and integrated development environment by JetBrains designed for creating and working with domain-specific languages using projectional editing.
  • 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: MPM-10
Triple: [Montreal Metro, rollingStock, MPM-10]
Generated description
MPM-10 is a modern rubber-tired metro train model used on the Montreal Metro, designed to increase capacity, comfort, and energy efficiency.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MPM-10
Target entity description: MPM-10 is a modern rubber-tired metro train model used on the Montreal Metro, designed to increase capacity, comfort, and energy efficiency.
  • A. MP 05
    MP 05 is a rubber-tyred, fully automated train model used on the Paris Métro, notably on Line 1 and Line 14.
  • B. MP 43
    MP 43 is an early German World War II assault rifle prototype that evolved into the famous Sturmgewehr 44, one of the first true assault rifles in history.
  • C. MP 14
    MP 14 is a modern rubber-tyred train model used on the Paris Métro, designed for improved energy efficiency, automation, and passenger comfort.
  • D. MPS
    MPS is a leading German research institute specializing in the study of the Sun and the solar system, operating under the Max Planck Society.
  • E. MPS
    MPS is a language workbench and integrated development environment by JetBrains designed for creating and working with domain-specific languages using projectional editing.
  • 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_69aed937fa8881908208ef3801060826 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aee3db11108190aa81ee8ed22709fe completed March 9, 2026, 3:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4f04353a881908e612a10572eb8c5 completed March 14, 2026, 5:21 a.m.
NEDg Description generation batch_69b4f0e77efc8190b4459d4559261a2f completed March 14, 2026, 5:23 a.m.
NED2 Entity disambiguation (via description) batch_69b4f15cfde88190bcb2680b90f98111 completed March 14, 2026, 5:25 a.m.
Created at: March 9, 2026, 3:13 p.m.