Triple

T1788197
Position Surface form Disambiguated ID Type / Status
Subject Paris Métro Line 5 E39435 entity
Predicate rollingStock P1305 FINISHED
Object MF 01
MF 01 is a class of modern steel-wheeled electric multiple unit trains used on several lines of the Paris Métro.
 E199145 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 01 | Statement: [Paris Métro Line 5, rollingStock, MF 01]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MF 01
Context triple: [Paris Métro Line 5, rollingStock, MF 01]
  • A. FMF
    FMF is the commonly used abbreviation for the Mexican Football Federation, the governing body of professional and amateur soccer in Mexico.
  • B. MFS
    MFS (Macintosh File System) is the original flat file system used by early Macintosh computers before the introduction of the hierarchical HFS.
  • C. MF
    MF is the two-letter IATA airline designator assigned to XiamenAir, a major Chinese carrier based in Xiamen.
  • D. MRF
    MRF (Media Resource Function) is a core network component in IP Multimedia Subsystem (IMS) architectures responsible for handling media processing tasks such as mixing, transcoding, and media stream manipulation for real-time communication services.
  • E. MI1
    MI1 was an early British military intelligence section responsible for handling secret information and code-related work before later intelligence agencies were formally established.
  • 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 01
Triple: [Paris Métro Line 5, rollingStock, MF 01]
Generated description
MF 01 is a class of modern steel-wheeled electric multiple unit trains used on several lines of the Paris Métro.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MF 01
Target entity description: MF 01 is a class of modern steel-wheeled electric multiple unit trains used on several lines of the Paris Métro.
  • A. FMF
    FMF is the commonly used abbreviation for the Mexican Football Federation, the governing body of professional and amateur soccer in Mexico.
  • B. MFS
    MFS (Macintosh File System) is the original flat file system used by early Macintosh computers before the introduction of the hierarchical HFS.
  • C. MF
    MF is the two-letter IATA airline designator assigned to XiamenAir, a major Chinese carrier based in Xiamen.
  • D. MRF
    MRF (Media Resource Function) is a core network component in IP Multimedia Subsystem (IMS) architectures responsible for handling media processing tasks such as mixing, transcoding, and media stream manipulation for real-time communication services.
  • E. MI1
    MI1 was an early British military intelligence section responsible for handling secret information and code-related work before later intelligence agencies were formally established.
  • 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_69a88631854081909723959921e45c2b completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa650fd3448190a6a2c979db982cae completed March 6, 2026, 5:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69ada9a8a69c8190885bf06a06d3869f completed March 8, 2026, 4:54 p.m.
NEDg Description generation batch_69adaab488ec81909a340aab4916b90f completed March 8, 2026, 4:58 p.m.
NED2 Entity disambiguation (via description) batch_69adaf3cd23081909dd27c5de8e3f6d2 completed March 8, 2026, 5:17 p.m.
Created at: March 4, 2026, 7:32 p.m.