Triple

T21511607
Position Surface form Disambiguated ID Type / Status
Subject أمانة العاصمة E530737 entity
Predicate تضم P1393 FINISHED
Object مدينة صنعاء 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: مدينة صنعاء | Statement: [أمانة العاصمة, تضم, مدينة صنعاء]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: مدينة صنعاء
Context triple: [أمانة العاصمة, تضم, مدينة صنعاء]
  • A. Sanaʽa chosen
    Sanaʽa is the historic capital and one of the largest cities of Yemen, renowned for its ancient architecture and cultural significance in the Arabian Peninsula.
  • B. Salalah
    Salalah is a coastal city in southern Oman known for its monsoon-cooled climate, lush green landscapes, and role as a regional tourism and commercial hub.
  • C. Saada city
    Saada city is a historic urban center in northern Yemen known for its traditional architecture and role as a stronghold of the Houthi movement.
  • D. Aden
    Aden was a small New York community that was permanently flooded and abandoned during the creation of the Neversink Reservoir in the mid-20th century.
  • E. Aden
    Aden is a strategic port city in Yemen located on the Gulf of Aden, historically significant as a major maritime hub and former British colonial stronghold.
  • 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_69e0c45c81f08190a6b8bbb70a45aae7 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69e9ea863b18819080e3ff249b10ec28 completed April 23, 2026, 9:46 a.m.
Created at: April 16, 2026, 6:25 p.m.