Triple

T12117261
Position Surface form Disambiguated ID Type / Status
Subject Rungus E288596 entity
Predicate region P40 FINISHED
Object Borneo E15167 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: Borneo | Statement: [Rungus, region, Borneo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Borneo
Context triple: [Rungus, region, Borneo]
  • A. Borneo chosen
    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.
  • B. Borneo Island
    Borneo Island is a modern residential island in Amsterdam’s Eastern Docklands, known for its contemporary architecture and waterfront urban design.
  • C. Kalimantan
    Kalimantan is the Indonesian portion of the island of Borneo, known for its vast rainforests, rich biodiversity, and significant natural resources.
  • D. northern Borneo
    Northern Borneo is the upper portion of the island of Borneo, encompassing parts of modern-day Malaysia and Brunei and known for its dense rainforests, rich biodiversity, and indigenous communities.
  • E. Borne Sulinowo
    Borne Sulinowo is a town in northwestern Poland known for having been a secret Soviet military base during the Cold War before being opened to civilians in the 1990s.
  • 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_69d6ab4a5c448190a110d1273314b21a completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d915760d208190b68f5e024b3676ba completed April 10, 2026, 3:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69f60a75f77881908345a585a689f69f completed May 2, 2026, 2:30 p.m.
Created at: April 8, 2026, 9:49 p.m.