Triple

T17645965
Position Surface form Disambiguated ID Type / Status
Subject Lampung Selatan Regency E429357 entity
Predicate hasProvinceCapitalNearby P28400 FINISHED
Object Bandar Lampung NE NERFINISHED

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: Bandar Lampung | Statement: [Lampung Selatan Regency, hasProvinceCapitalNearby, Bandar Lampung]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bandar Lampung
Context triple: [Lampung Selatan Regency, hasProvinceCapitalNearby, Bandar Lampung]
  • A. Bandar Lampung chosen
    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.
  • B. Pangkalpinang
    Pangkalpinang is the largest city and administrative, economic, and cultural center of Indonesia’s Bangka Belitung Islands province, located on Bangka Island.
  • C. Cilegon
    Cilegon is an industrial port city in western Java, Indonesia, known for its steel industry and strategic location near the Sunda Strait.
  • D. Tangerang
    Tangerang is a major urban and industrial city in Indonesia located just west of Jakarta on the island of Java.
  • E. Batam
    Batam is a major Indonesian industrial and transport hub located near Singapore, known for its free-trade zone status and rapidly growing economy.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d889e2c2608190b762e76d9b2262f1 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e46e382ba88190af19d0e3b8c8cadd completed April 19, 2026, 5:55 a.m.
Created at: April 10, 2026, 6:04 a.m.