Triple

T9930036
Position Surface form Disambiguated ID Type / Status
Subject Bangka E192623 entity
Predicate hasLanguage P15 FINISHED
Object Bangka Malay E192623 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: Bangka Malay | Statement: [Bangka, hasLanguage, Bangka Malay]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bangka Malay
Context triple: [Bangka, hasLanguage, Bangka Malay]
  • A. Bangka chosen
    Bangka is a large Indonesian island off the east coast of Sumatra, known for its tin mining and beautiful beaches.
  • B. Bantia
    Bantia was an ancient Oscan-speaking city in southern Italy, notable for yielding important inscriptions that illuminate the Oscan language and Italic legal traditions.
  • C. Alam Melayu
    Alam Melayu refers to the cultural and historical Malay world encompassing the Malay-speaking peoples and regions of Maritime Southeast Asia.
  • D. Nusajaya
    Nusajaya is a planned township in Johor, Malaysia, developed as a key administrative, commercial, and residential hub within the Iskandar Malaysia economic corridor.
  • E. Bachok
    Bachok is a coastal town and district in the Malaysian state of Kelantan, known for its beaches and traditional Malay fishing villages.
  • 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_69ca82dd978c8190947124ab0d3315ac completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cdb5b4196881909a004091a4203c45 completed April 2, 2026, 12:17 a.m.
NED1 Entity disambiguation (via context triple) batch_69d20e258e888190ae4e2abac80e3399 completed April 5, 2026, 7:24 a.m.
Created at: March 30, 2026, 8:43 p.m.