Triple

T9930025
Position Surface form Disambiguated ID Type / Status
Subject Bangka E192623 entity
Predicate capitalOfProvinceLocatedOn P3433 FINISHED
Object Pangkal Pinang 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: Pangkal Pinang | Statement: [Bangka, capitalOfProvinceLocatedOn, Pangkal Pinang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pangkal Pinang
Context triple: [Bangka, capitalOfProvinceLocatedOn, Pangkal Pinang]
  • 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. Batam
    Batam is a major Indonesian industrial and transport hub located near Singapore, known for its free-trade zone status and rapidly growing economy.
  • D. 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.
  • 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_69ca82dd978c8190947124ab0d3315ac completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cdb5b4196881909a004091a4203c45 completed April 2, 2026, 12:17 a.m.
NED1 Entity disambiguation (via context triple) batch_69d228cab0fc81908ff5fad6916c1bab completed April 5, 2026, 9:18 a.m.
Created at: March 30, 2026, 8:43 p.m.