Triple

T23060949
Position Surface form Disambiguated ID Type / Status
Subject Paitanic languages E574298 entity
Predicate region P40 FINISHED
Object Sabah NE NERFINISHED

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: [Paitanic languages, region, Sabah]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sabah
Context triple: [Paitanic languages, region, 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. Sabah
    Sabah is a major Turkish daily newspaper known for its wide circulation and coverage of national news, politics, and entertainment.
  • C. 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.
  • D. Kayah State
    Kayah State is a small, mountainous administrative region in eastern Myanmar known for its diverse ethnic communities, including various Karen groups, and its long history of political marginalization and conflict.
  • E. Tawau
    Tawau is a coastal town and major economic hub in southeastern Sabah, Malaysia, known for its port, agriculture, and proximity to Indonesia.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e245ba7ae48190be606dbc54120e39 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f1899ff96081908d89a07a3b1065c8 completed April 29, 2026, 4:31 a.m.
Created at: April 17, 2026, 3:55 p.m.