Triple

T11091161
Position Surface form Disambiguated ID Type / Status
Subject Fayez E262256 entity
Predicate hasVariantTransliteration P5923 FINISHED
Object Faez E904200 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: Faez | Statement: [Fayez, hasVariantTransliteration, Faez]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Faez
Context triple: [Fayez, hasVariantTransliteration, Faez]
  • A. Fayiz chosen
    Fayiz is a masculine given name of Arabic origin, commonly used as a variant transliteration of the name Fayez.
  • B. Faiha
    Faiha is a residential district in Kuwait City known for its planned layout, community facilities, and central location within the capital.
  • C. Fahdah
    Fahdah is a Saudi princess, formally known as Princess Fahdah Mohammed Abunayyan, associated with the Saudi royal family.
  • D. Unaizah
    Unaizah is a historic oasis city in central Saudi Arabia’s Qassim region, known for its date farms, traditional markets, and cultural heritage.
  • E. Faizi
    Faizi was a renowned 16th-century Persian-language poet and scholar who served as one of the prominent intellectuals in the Mughal emperor Akbar’s court.
  • 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_69d6aa9a40d88190a373e2c7e48285db completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d799ebae8c8190987b474adb7ede47 completed April 9, 2026, 12:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69e441bb14d08190ac01bf3daa34ae43 completed April 19, 2026, 2:45 a.m.
Created at: April 8, 2026, 9:27 p.m.