Triple

T304589
Position Surface form Disambiguated ID Type / Status
Subject Sundanese E6269 entity
Predicate primaryRegion P1103 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: [Sundanese, primaryRegion, Jakarta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jakarta
Context triple: [Sundanese, primaryRegion, 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. 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.
  • E. Surabaya
    Surabaya is Indonesia’s second-largest city and a key commercial and industrial hub on the island of Java, historically serving as one of the region’s most important seaports.
  • 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_69a2e79230508190b912ecb555aae17e completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2ea1032e48190864338e030d9dc92 completed Feb. 28, 2026, 1:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3b07566c88190af82b1953902b613 completed March 1, 2026, 3:20 a.m.
Created at: Feb. 28, 2026, 1:06 p.m.