Triple

T20683859
Position Surface form Disambiguated ID Type / Status
Subject RMT E508363 entity
Predicate hasAbbreviation P43 FINISHED
Object RMT NE NERFINISHED

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: RMT | Statement: [RMT, hasAbbreviation, RMT]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: RMT
Context triple: [RMT, hasAbbreviation, RMT]
  • A. RMT chosen
    RMT is the commonly used abbreviation for the United Nations Conference on Trade and Development’s annual Review of Maritime Transport report, which analyzes global maritime trade and shipping trends.
  • B. RTM
    RTM is the commonly used abbreviation for Rosetta Terminology Mapping, a system for standardizing and aligning terminology across different datasets or domains.
  • C. RTM
    RTM is the IATA airport code for Rotterdam The Hague Airport, a regional international airport serving the Rotterdam–The Hague metropolitan area in the Netherlands.
  • D. RTM
    RTM is the public transport authority that operates buses, trams, and metro services in Marseille and its surrounding metropolitan area in France.
  • E. RMF
    RMF (Resource Measurement Facility) is an IBM performance monitoring and reporting tool used to analyze and manage system resources on z/OS mainframe environments.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4c1ed408190b72dd26b1e33f8a1 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6beaae5608190ac8cc64aa4717d53 completed April 21, 2026, 12:02 a.m.
Created at: April 16, 2026, 11:45 a.m.