Triple

T15042449
Position Surface form Disambiguated ID Type / Status
Subject Martapura River E378634 entity
Predicate flowsThrough P225 FINISHED
Object Martapura E843161 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: Martapura | Statement: [Martapura River, flowsThrough, Martapura]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Martapura
Context triple: [Martapura River, flowsThrough, Martapura]
  • A. Martapura chosen
    Martapura is a prominent town in Indonesia’s South Kalimantan province, known as a center for Islamic education and its traditional diamond and gemstone markets.
  • B. Banjarbaru
    Banjarbaru is a rapidly developing city in Indonesia that serves as the capital and administrative center of South Kalimantan province on the island of Borneo.
  • 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. Kotabaru
    Kotabaru is a coastal town and regency capital in Indonesia known for its strategic location on Laut Island and its role as an economic and administrative hub in the region.
  • E. 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.
  • 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_69d85cd46b2c819090d054c27787f677 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded82f73208190bb55fa6b20074e27 completed April 15, 2026, 12:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69fea5b75c04819085996a7f88ab6c38 completed May 9, 2026, 3:10 a.m.
Created at: April 10, 2026, 3 a.m.