Triple

T19721510
Position Surface form Disambiguated ID Type / Status
Subject Ifrane Province E473620 entity
Predicate containsCity P294 FINISHED
Object Azrou 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: Azrou | Statement: [Ifrane Province, containsCity, Azrou]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Azrou
Context triple: [Ifrane Province, containsCity, Azrou]
  • A. Azrou chosen
    Azrou is a small Moroccan town in the Middle Atlas mountains, known for its cedar forests, Berber culture, and nearby Barbary macaque populations.
  • B. Azilal
    Azilal is a town in central Morocco known as a gateway to the Atlas Mountains and nearby natural attractions such as waterfalls and hiking areas.
  • C. Taounate
    Taounate is a prominent city in northern Morocco known as an administrative and commercial center for the surrounding rural and mountainous region.
  • D. Sefrou
    Sefrou is a historic town in northern Morocco known for its traditional medina, cherry festival, and location near the Middle Atlas mountains.
  • E. Tiznit
    Tiznit is a historic town in southern Morocco known for its traditional silver jewelry craftsmanship and fortified old medina.
  • 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_69d8e516dd048190a0b6c93ea3e71f58 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e649f483c481908c6b3114bf9c5934 completed April 20, 2026, 3:44 p.m.
Created at: April 10, 2026, 1:46 p.m.