Triple

T9484608
Position Surface form Disambiguated ID Type / Status
Subject South Klang Valley Expressway E228726 entity
Predicate connectsTo P845 FINISHED
Object Banting E802160 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: Banting | Statement: [South Klang Valley Expressway, connectsTo, Banting]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Banting
Context triple: [South Klang Valley Expressway, connectsTo, Banting]
  • A. Banting
    Banting is a surname most famously associated with Frederick Banting, the Canadian physician and Nobel laureate who co-discovered insulin.
  • B. Banting chosen
    Banting is a town in the Kuala Langat District of Selangor, Malaysia, known as a growing commercial and residential hub connected to major routes in the South Klang Valley region.
  • C. Tuban
    Tuban is a coastal town and regency capital in northern East Java, Indonesia, known historically as a trading port and for its cultural and religious heritage sites.
  • D. Kepanjen
    Kepanjen is a town in East Java, Indonesia, known as an administrative and growing urban center within the Malang region.
  • E. Magetan
    Magetan is a regency and town in East Java, Indonesia, known for its cool climate, agricultural production, and proximity to the scenic Sarangan Lake and Mount Lawu.
  • 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_69ca84730a5081908de282651019bf2f completed March 30, 2026, 2:10 p.m.
NER Named-entity recognition batch_69cd804e278c8190b1f869158075cd52 completed April 1, 2026, 8:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69d139f7fa90819092e3fbcc62a9e5b9 completed April 4, 2026, 4:19 p.m.
Created at: March 30, 2026, 7:55 p.m.