Triple

T1841561
Position Surface form Disambiguated ID Type / Status
Subject Paris Metro E41186 entity
Predicate hasRollingStock P1305 FINISHED
Object MF 77
MF 77 is a steel-wheeled electric multiple unit train used on several lines of the Paris Métro, introduced in the late 1970s to modernize the network’s rolling stock.
 E207261 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: MF 77 | Statement: [Paris Metro, hasRollingStock, MF 77]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MF 77
Context triple: [Paris Metro, hasRollingStock, MF 77]
  • A. MF 67
    MF 67 is a class of steel-wheeled electric multiple unit trains that have long served as a primary rolling stock type on the Paris Métro.
  • B. M-72
    M-72 is a state highway in northern Michigan that serves as a key east–west route connecting Traverse City with several inland communities and recreational areas.
  • C. MF 01
    MF 01 is a class of modern steel-wheeled electric multiple unit trains used on several lines of the Paris Métro.
  • D. M-45
    M-45 is a state highway in Michigan that runs east–west through the Grand Rapids area toward the Lake Michigan shoreline.
  • E. M-22
    M-22 is a scenic state highway in Michigan renowned for its picturesque route along the Lake Michigan shoreline and through the Leelanau Peninsula near Traverse City.
  • 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: MF 77
Triple: [Paris Metro, hasRollingStock, MF 77]
Generated description
MF 77 is a steel-wheeled electric multiple unit train used on several lines of the Paris Métro, introduced in the late 1970s to modernize the network’s rolling stock.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MF 77
Target entity description: MF 77 is a steel-wheeled electric multiple unit train used on several lines of the Paris Métro, introduced in the late 1970s to modernize the network’s rolling stock.
  • A. MF 67
    MF 67 is a class of steel-wheeled electric multiple unit trains that have long served as a primary rolling stock type on the Paris Métro.
  • B. M-72
    M-72 is a state highway in northern Michigan that serves as a key east–west route connecting Traverse City with several inland communities and recreational areas.
  • C. MF 01
    MF 01 is a class of modern steel-wheeled electric multiple unit trains used on several lines of the Paris Métro.
  • D. M-45
    M-45 is a state highway in Michigan that runs east–west through the Grand Rapids area toward the Lake Michigan shoreline.
  • E. M-22
    M-22 is a scenic state highway in Michigan renowned for its picturesque route along the Lake Michigan shoreline and through the Leelanau Peninsula near Traverse City.
  • 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_69a88647f9388190909bc36e795bdaec completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb03e7a7481909c5b902034390ef1 completed March 7, 2026, 4:57 a.m.
NED1 Entity disambiguation (via context triple) batch_69add1c482908190b940497fc5d5db60 completed March 8, 2026, 7:45 p.m.
NEDg Description generation batch_69add2ac44f48190bd62c504b31210e8 completed March 8, 2026, 7:49 p.m.
NED2 Entity disambiguation (via description) batch_69add305bd108190b5e7e0f3d30a2c58 completed March 8, 2026, 7:50 p.m.
Created at: March 4, 2026, 7:33 p.m.