Triple

T5933232
Position Surface form Disambiguated ID Type / Status
Subject Bencoolen E131985 entity
Predicate locatedIn 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: [Bencoolen, locatedIn, Sumatra]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sumatra
Context triple: [Bencoolen, locatedIn, 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. Isle of Java
    Isle of Java is a coffee-focused quick-service location at Disney’s Discovery Island, known for serving beverages and light refreshments to park guests.
  • E. 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.
  • 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_69c0085c55dc8190aa90e242c956e2fa completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c0389f6fc881909527b928838ffcdd completed March 22, 2026, 6:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0bf8fc8b881908c600c2f7de0d691 completed March 23, 2026, 4:20 a.m.
Created at: March 22, 2026, 4 p.m.