Triple

T9012992
Position Surface form Disambiguated ID Type / Status
Subject Klang Valley E215519 entity
Predicate hasTransportInfrastructure P2560 FINISHED
Object MRT network E576622 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: MRT network | Statement: [Klang Valley, hasTransportInfrastructure, MRT network]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: MRT network
Context triple: [Klang Valley, hasTransportInfrastructure, MRT network]
  • A. MRT subway
    The MRT subway in Bangkok is a major rapid transit system that provides fast, air-conditioned underground and elevated rail services across key areas of the city.
  • B. MRT
    MRT is the three-letter ISO 3166-1 alpha-3 country code assigned to Mauritania.
  • C. MRT
    MRT is the commonly used abbreviation for the Taipei Metro, the rapid transit system serving Taipei and its surrounding areas.
  • D. MRT Grey Line
    The MRT Grey Line is a planned rapid transit route within the MRT subway network designed to expand urban rail connectivity along its corridor.
  • E. Mass Rapid Transit (MRT) network of Singapore chosen
    The Mass Rapid Transit (MRT) network of Singapore is a high-capacity urban rail system that forms the backbone of the city-state’s public transport, efficiently connecting key residential, commercial, and industrial areas.
  • 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_69ca83a2bf088190986ee7a8eb90407d completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc69f96980819093bfc49d48570c65 completed April 1, 2026, 12:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfdba11a6481909cac624530a77ffe completed April 3, 2026, 3:24 p.m.
Created at: March 30, 2026, 7:06 p.m.