Triple

T730798
Position Surface form Disambiguated ID Type / Status
Subject Medan E14825 entity
Predicate locatedOn P40 FINISHED
Object Sumatra 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 | Statement: [Medan, locatedOn, Sumatra]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sumatra
Context triple: [Medan, locatedOn, Sumatra]
  • 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. Kalimantan
    Kalimantan is the Indonesian portion of the island of Borneo, known for its vast rainforests, rich biodiversity, and significant natural resources.
  • D. Greater Sunda Islands
    The Greater Sunda Islands are a major group of large islands in maritime Southeast Asia, including Java, Sumatra, Borneo, and Sulawesi, known for their rich biodiversity and dense human populations.
  • E. Borneo
    Borneo is the world’s third-largest island in Southeast Asia, known for its vast rainforests, rich biodiversity, and division among Indonesia, Malaysia, and Brunei.
  • 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_69a4934d9930819099eed80096b0597d completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a5c40b6481909db9efd7310850b3 completed March 1, 2026, 8:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7cf4b62b08190bc8d5978595ce60b completed March 4, 2026, 6:20 a.m.
Created at: March 1, 2026, 7:37 p.m.