Triple

T1479680
Position Surface form Disambiguated ID Type / Status
Subject Nusa Tenggara E30923 entity
Predicate hasPart P35 FINISHED
Object Lombok E20548 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: Lombok | Statement: [Nusa Tenggara, hasPart, Lombok]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lombok
Context triple: [Nusa Tenggara, hasPart, Lombok]
  • A. Lombok chosen
    Lombok is an Indonesian island east of Bali, known for its volcanic Mount Rinjani, beaches, and Sasak culture.
  • B. Hatta
    Hatta is an Indonesian surname most prominently associated with Mohammad Hatta, the country’s first vice president and a leading figure in the struggle for independence.
  • C. Yanam
    Yanam is a coastal town and district enclave of the Union Territory of Puducherry in India, historically influenced by French colonial rule and culturally linked to the Telugu-speaking region of Andhra Pradesh.
  • D. Lapa
    Lapa is a historic and bohemian neighborhood in Rio de Janeiro, Brazil, famous for its vibrant nightlife, samba clubs, and iconic aqueduct arches.
  • E. Madura
    Madura is an island off the northeastern coast of Java in Indonesia, known for its distinct Madurese culture and traditional bull races.
  • 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_69a498fe55a88190ab7f9e40ace88e49 completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c674cc9c819088fc9146c7a7a914 completed March 1, 2026, 11:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad1ca21f288190b5f6f9a5895cdcf0 completed March 8, 2026, 6:52 a.m.
Created at: March 1, 2026, 8:11 p.m.