Triple

T10824099
Position Surface form Disambiguated ID Type / Status
Subject Mutayr E255449 entity
Predicate traditionalDialect P1762 FINISHED
Object Najdi Arabic E36756 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: Najdi Arabic | Statement: [Mutayr, traditionalDialect, Najdi Arabic]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Najdi Arabic
Context triple: [Mutayr, traditionalDialect, Najdi Arabic]
  • A. Najdi Arabic chosen
    Najdi Arabic is a central Arabian dialect of the Arabic language spoken primarily in the Najd region of Saudi Arabia.
  • B. Razihi Arabic
    Razihi Arabic is a highly distinctive and conservative Arabic variety spoken in the Razih region of northwestern Yemen, noted for preserving archaic linguistic features.
  • C. Shua Arabic
    Shua Arabic is a variety of Arabic spoken primarily by nomadic and semi-nomadic communities in Chad and neighboring regions of Central Africa.
  • D. Arabic
    Arabic is a Semitic language widely spoken across the Arab world and used as a liturgical language in Islam.
  • E. Badawi Najdi Arabic
    Badawi Najdi Arabic is a Bedouin variety of the Najdi Arabic dialect spoken primarily by nomadic and tribal communities in central Arabia.
  • 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_69d6aa8081448190a9324184f2bd1c26 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d734cf7918819094d36ea208c80d12 completed April 9, 2026, 5:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69de858672d8819094baf4fe98b8dea4 completed April 14, 2026, 6:20 p.m.
Created at: April 8, 2026, 9:19 p.m.