Triple

T5618005
Position Surface form Disambiguated ID Type / Status
Subject Rumi E147525 entity
Predicate contrastWith P278 FINISHED
Object Jawi E118143 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: Jawi | Statement: [Rumi, contrastWith, Jawi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jawi
Context triple: [Rumi, contrastWith, Jawi]
  • A. Jawi script chosen
    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.
  • B. Javanese script
    The Javanese script is a traditional Brahmic-derived abugida used historically and culturally for writing the Javanese language, especially on the island of Java in Indonesia.
  • C. Jawi Malay
    Jawi Malay is a historical form of the Malay language written in the Arabic-based Jawi script, used as a key administrative and literary medium in the Malay world.
  • D. Sundanese script
    The Sundanese script is an abugida used historically and in modern times to write the Sundanese language of West Java, Indonesia.
  • E. 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.
  • 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_69c00905d4588190bd967842bbcf2219 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c021dc04c081909f36e40394e7c955 completed March 22, 2026, 5:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0287d9d7081909a1b2510565b55fc completed March 22, 2026, 5:35 p.m.
Created at: March 22, 2026, 3:40 p.m.