Triple

T1841562
Position Surface form Disambiguated ID Type / Status
Subject Paris Metro E41186 entity
Predicate hasRollingStock P1305 FINISHED
Object MF 01 E199145 NE FINISHED

How this triple was built (2 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 Metro, hasRollingStock, MF 01]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: MF 01
Context triple: [Paris Metro, hasRollingStock, MF 01]
  • A. MF 01 chosen
    MF 01 is a class of modern steel-wheeled electric multiple unit trains used on several lines of the Paris Métro.
  • B. FMF
    FMF is the commonly used abbreviation for the Mexican Football Federation, the governing body of professional and amateur soccer in Mexico.
  • C. MFS
    MFS (Macintosh File System) is the original flat file system used by early Macintosh computers before the introduction of the hierarchical HFS.
  • D. MF
    MF is the two-letter IATA airline designator assigned to XiamenAir, a major Chinese carrier based in Xiamen.
  • E. 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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69adc9bb92a88190a00b102d3be0383c completed March 8, 2026, 7:10 p.m.
Created at: March 4, 2026, 7:33 p.m.