Triple

T1375697
Position Surface form Disambiguated ID Type / Status
Subject Sarawak E29216 entity
Predicate borderedBy P224 FINISHED
Object Sabah E57487 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: Sabah | Statement: [Sarawak, borderedBy, Sabah]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sabah
Context triple: [Sarawak, borderedBy, Sabah]
  • A. Sabah chosen
    Sabah is a Malaysian state on the northern portion of Borneo, known for its rich biodiversity, indigenous cultures, and iconic Mount Kinabalu.
  • B. Sarawak
    Sarawak is a resource-rich Malaysian state on the island of Borneo, known for its diverse indigenous cultures, extensive rainforests, and long history under the rule of the White Rajahs before joining Malaysia.
  • C. Kainan
    Kainan is a coastal city in central Wakayama Prefecture, Japan, known for its traditional industries and scenic seaside setting.
  • D. Aceh
    Aceh is a special autonomous province at the northern tip of the Indonesian island of Sumatra, known for its distinct Islamic heritage and historical role as a major trading center.
  • E. Pahang
    Pahang is a large Malaysian state on the eastern coast of Peninsular Malaysia, known for its extensive rainforests, highlands like Cameron Highlands, and long South China Sea coastline.
  • 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_69a498d883a48190bfdca525296ef7ee completed March 1, 2026, 7:51 p.m.
NER Named-entity recognition batch_69a4c2f9b51c8190ad52fd8c151499be completed March 1, 2026, 10:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69acde1c7e5c8190a6b999f2d5dce088 completed March 8, 2026, 2:25 a.m.
Created at: March 1, 2026, 7:59 p.m.