Triple

T17193476
Position Surface form Disambiguated ID Type / Status
Subject Mende language E417285 entity
Predicate hasScript P182 FINISHED
Object Kikakui script E581903 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: Kikakui script | Statement: [Mende language, hasScript, Kikakui script]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kikakui script
Context triple: [Mende language, hasScript, Kikakui script]
  • A. Kikakui script chosen
    The Kikakui script is an indigenous syllabary developed in the 19th century for writing the Mende language of Sierra Leone.
  • B. Bugis script
    Bugis script is a traditional Brahmic writing system used primarily for the Buginese language of South Sulawesi, Indonesia.
  • C. Kawi script
    Kawi script is an ancient Brahmic-derived writing system historically used across Java and other parts of Southeast Asia to write Old Javanese and related languages.
  • D. Sorabe script
    The Sorabe script is an Arabic-derived writing system historically used by Malagasy speakers, particularly in southern Madagascar, for religious, literary, and administrative texts.
  • E. Tai Nüa script
    The Tai Nüa script is an abugida used primarily by the Tai Nüa (Dai) people of China and Southeast Asia to write the Tai Nüa language.
  • 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_69d886d6ba8c819093215917b3d01689 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e42da93bf88190b60b658087779d36 completed April 19, 2026, 1:19 a.m.
NED1 Entity disambiguation (via context triple) batch_6a01674a3a78819094c093daac0e508d completed May 11, 2026, 5:21 a.m.
Created at: April 10, 2026, 5:38 a.m.