Triple

T1385206
Position Surface form Disambiguated ID Type / Status
Subject West Java E29829 entity
Predicate borders P224 FINISHED
Object Jakarta E29483 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: Jakarta | Statement: [West Java, borders, Jakarta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jakarta
Context triple: [West Java, borders, Jakarta]
  • A. Jakarta chosen
    Jakarta is the bustling capital and largest city of Indonesia, serving as the country’s political, economic, and cultural center on the island of Java.
  • B. Bogor
    Bogor is a city on the Indonesian island of Java known for its cool climate, botanical gardens, and role as a major educational and research center.
  • C. Yogyakarta
    Yogyakarta is a major cultural and educational city on the Indonesian island of Java, renowned for its traditional arts, universities, and proximity to the Borobudur and Prambanan temples.
  • D. Greater Jakarta
    Greater Jakarta is Indonesia’s largest metropolitan area, encompassing Jakarta and its surrounding cities and suburbs as the country’s primary political, economic, and urban hub.
  • E. Bandung
    Bandung is a large Indonesian city on the island of Java known for its cool climate, universities, colonial and art deco architecture, and role as a center of culture and technology.
  • 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_69a498dc92f8819094a1108f8ac90f43 completed March 1, 2026, 7:51 p.m.
NER Named-entity recognition batch_69a4c33896548190b44f70c9aaaed9b6 completed March 1, 2026, 10:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad36fb2c348190860129f84ca59cc3 completed March 8, 2026, 8:44 a.m.
Created at: March 1, 2026, 7:59 p.m.