Triple

T5119889
Position Surface form Disambiguated ID Type / Status
Subject Bangka Belitung Islands E115434 entity
Predicate hasCity P316 FINISHED
Object Pangkalpinang E495114 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: Pangkalpinang | Statement: [Bangka Belitung Islands, hasCity, Pangkalpinang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pangkalpinang
Context triple: [Bangka Belitung Islands, hasCity, Pangkalpinang]
  • A. Pangkalpinang chosen
    Pangkalpinang is the largest city and administrative, economic, and cultural center of Indonesia’s Bangka Belitung Islands province, located on Bangka Island.
  • B. Tanjung Pinang
    Tanjung Pinang is a coastal city in Indonesia located on Bintan Island, known as an administrative and commercial hub in the Riau Islands province.
  • C. Bandar Lampung
    Bandar Lampung is a major port city in southern Sumatra, Indonesia, serving as the capital of Lampung Province and a key gateway between the island and Java.
  • D. Batam
    Batam is a major Indonesian industrial and transport hub located near Singapore, known for its free-trade zone status and rapidly growing economy.
  • E. Pekanbaru
    Pekanbaru is a major commercial and transportation hub in central Sumatra, Indonesia, known for its oil industry and rapid urban growth.
  • 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_69bd4442ade0819087b9461f892b206b completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd77cf6590819081488b739efae32c completed March 20, 2026, 4:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69becfcf12448190a196e9397958fbba completed March 21, 2026, 5:05 p.m.
Created at: March 20, 2026, 1:42 p.m.