Triple

T7257372
Position Surface form Disambiguated ID Type / Status
Subject Kambera language E157754 entity
Predicate hasDialect P4251 FINISHED
Object Kambera proper E652671 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: Kambera proper | Statement: [Kambera language, hasDialect, Kambera proper]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kambera proper
Context triple: [Kambera language, hasDialect, Kambera proper]
  • A. Kambera chosen
    Kambera is an Austronesian language spoken primarily on the island of Sumba in eastern Indonesia.
  • B. Kamberi
    Kamberi are an ethnic group in northwestern Nigeria, primarily inhabiting rural areas of Kebbi State and known for their distinct language and traditional cultural practices.
  • C. Negombo
    Negombo is a coastal city in western Sri Lanka known historically as a strategic colonial port and today for its fishing industry and beach tourism.
  • D. Candaba
    Candaba is a municipality in the Philippine province of Pampanga known for its vast wetlands and rich bird-watching sites.
  • E. Kombe
    Kombe is a Bantu language spoken by the Kombe people of coastal Equatorial Guinea and nearby regions, closely related to other Ndowe-area languages.
  • 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_69c6882d81d4819085f7ff862951ee4f completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6eaa3d88081908f59ca5a85790290 completed March 27, 2026, 8:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7db14f6c481908084aaa49d82787d completed March 28, 2026, 1:43 p.m.
Created at: March 27, 2026, 2:57 p.m.