Triple

T21938183
Position Surface form Disambiguated ID Type / Status
Subject Lont E541747 entity
Predicate scriptAlternateName P110696 FINISHED
Object Bugis script 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: Bugis script | Statement: [Lont, scriptAlternateName, Bugis script]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bugis script
Context triple: [Lont, scriptAlternateName, Bugis script]
  • A. Bugis script chosen
    Bugis script is a traditional Brahmic writing system used primarily for the Buginese language of South Sulawesi, Indonesia.
  • B. 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.
  • C. 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.
  • D. Kikakui script
    The Kikakui script is an indigenous syllabary developed in the 19th century for writing the Mende language of Sierra Leone.
  • E. Jawi script
    Jawi script is an Arabic-based writing system historically used for various Malayic languages in Southeast Asia, including Minangkabau, for religious, literary, and administrative purposes.
  • 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_69e0c47e2e5c81909a7f74ce3de50911 completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f1241e35bc81909eb3225d5cd97b92 completed April 28, 2026, 9:18 p.m.
Created at: April 16, 2026, 7:55 p.m.