Triple

T20786868
Position Surface form Disambiguated ID Type / Status
Subject جامع القيروان الكبير E511661 entity
Predicate يقع_في P40 FINISHED
Object تونس 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: تونس | Statement: [جامع القيروان الكبير, يقع_في, تونس]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: تونس
Context triple: [جامع القيروان الكبير, يقع_في, تونس]
  • A. Tunis chosen
    Tunis is the capital and largest city of Tunisia, serving as a major political, economic, and cultural center in the Arab world.
  • B. Sfax
    Sfax is a major port city on Tunisia’s eastern coast, known as an economic hub and a significant center of political activism during the Tunisian Revolution.
  • C. Tunis Governorate
    Tunis Governorate is an administrative region in northeastern Tunisia that encompasses the nation’s capital city, Tunis, and serves as its political and economic center.
  • D. Mahdia
    Mahdia is a historic coastal city in present-day Tunisia that served as the first capital of the Fatimid Caliphate and an important Mediterranean trading and naval center.
  • E. Sidi Bouzid
    Sidi Bouzid is a coastal town in western Morocco known for its beaches and seaside tourism.
  • 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_69e0b4cb83948190bd57bec21d78ed53 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c28d24708190bf3890a22d1ec4b7 completed April 21, 2026, 12:19 a.m.
Created at: April 16, 2026, 12:38 p.m.