Triple

T1806110
Position Surface form Disambiguated ID Type / Status
Subject Saint Catherine E40222 entity
Predicate officialLanguage P236 FINISHED
Object Arabic E1330 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: Arabic | Statement: [Saint Catherine, officialLanguage, Arabic]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Arabic
Context triple: [Saint Catherine, officialLanguage, Arabic]
  • A. Arabic chosen
    Arabic is a Semitic language widely spoken across the Arab world and used as a liturgical language in Islam.
  • B. Hijazi Arabic
    Hijazi Arabic is a major regional variety of Arabic spoken primarily in western Saudi Arabia, especially in the Hijaz region including cities like Mecca, Medina, and Jeddah.
  • C. 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.
  • D. Egyptian Arabic
    Egyptian Arabic is the most widely understood modern Arabic dialect, centered in Egypt and heavily influenced by the speech and media of Cairo.
  • E. Sanʽani Arabic
    Sanʽani Arabic is a distinctive variety of Yemeni Arabic spoken in and around the city of Sanaʽa, known for its conservative linguistic features and unique phonology within the Arabic dialect continuum.
  • 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_69a88643a3388190a612f2ebe1fb29e7 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa659798b88190bd3070349ce6bebb completed March 6, 2026, 5:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69adb5df20948190943a60c209270b62 completed March 8, 2026, 5:46 p.m.
Created at: March 4, 2026, 7:32 p.m.