Triple

T4302663
Position Surface form Disambiguated ID Type / Status
Subject Lampung E99876 entity
Predicate partOf P40 FINISHED
Object Sumatra region E14825 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: Sumatra region | Statement: [Lampung, partOf, Sumatra region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sumatra region
Context triple: [Lampung, partOf, Sumatra region]
  • A. Sumatra chosen
    Sumatra is a large Indonesian island in western Indonesia known for its rich biodiversity, active volcanoes, and significant role in regional trade and history.
  • B. North Sumatra
    North Sumatra is a populous province on the Indonesian island of Sumatra, known for its diverse cultures, Lake Toba, and the city of Medan as its capital.
  • C. Sulawesi region
    The Sulawesi region is a large, mountainous island area in central Indonesia known for its distinctive peninsular shape, rich marine and terrestrial biodiversity, and cultural and linguistic diversity.
  • D. South Sumatra
    South Sumatra is a province in the southern part of the Indonesian island of Sumatra, known for its capital Palembang and its rich natural resources and cultural heritage.
  • E. central Sumatra
    Central Sumatra is a region in the middle of Indonesia’s Sumatra Island known for its mix of urban centers, tropical forests, and resource-rich landscapes.
  • 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_69b345528ebc8190b5abc7e95094792d completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b350b66450819089c9ff6ff9f045e5 completed March 12, 2026, 11:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69b63734dee08190a9c0e051a28d9177 completed March 15, 2026, 4:36 a.m.
Created at: March 12, 2026, 11:08 p.m.