Triple

T4141433
Position Surface form Disambiguated ID Type / Status
Subject Mount Kerinci E89279 entity
Predicate locatedIn P40 FINISHED
Object Jambi E94728 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: Jambi | Statement: [Mount Kerinci, locatedIn, Jambi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jambi
Context triple: [Mount Kerinci, locatedIn, Jambi]
  • A. Jambi chosen
    Jambi is a province on the eastern coast of central Sumatra in Indonesia, known for its oil production, plantations, and the Batanghari River.
  • B. Bengkulu
    Bengkulu is a province on the southwest coast of the Indonesian island of Sumatra, known for its Indian Ocean shoreline and colonial history.
  • C. Padang Besar
    Padang Besar is a border town in northern Malaysia known as a key land gateway and trading hub between Malaysia and Thailand.
  • D. Banjarmasin
    Banjarmasin is a major riverine city in South Kalimantan, Indonesia, known for its historic floating markets and strategic location on the island of Borneo.
  • E. Lampung
    Lampung is a province at the southern tip of the Indonesian island of Sumatra, known for its coastal landscapes, agriculture, and proximity to the Sunda Strait.
  • 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_69aed95785788190ae75bcf0cd1cafdf completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af024b8fe4819098e8f393474363c8 completed March 9, 2026, 5:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69b589e13e5881909c52e04875afc542 completed March 14, 2026, 4:16 p.m.
Created at: March 9, 2026, 3:43 p.m.