Triple

T11068400
Position Surface form Disambiguated ID Type / Status
Subject Islam in Yemen E261682 entity
Predicate importantCity P3940 FINISHED
Object Taiz E113390 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: Taiz | Statement: [Islam in Yemen, importantCity, Taiz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Taiz
Context triple: [Islam in Yemen, importantCity, Taiz]
  • A. Taiz chosen
    Taiz is one of Yemen’s largest and historically most important cities, known as a cultural and intellectual center in the country.
  • B. Arafo
    Arafo is a small municipality on the island of Tenerife in Spain’s Canary Islands, known for its rural landscapes and traditional Canarian character.
  • C. Berrechid
    Berrechid is a rapidly growing city in northwestern Morocco known as an important agricultural and industrial hub within the Casablanca-Settat region.
  • D. Hamina
    Hamina is a coastal town and municipality in southeastern Finland known for its historic star-shaped fortress and strategic location on the Gulf of Finland.
  • E. Taroudant
    Taroudant is a historic walled city in southern Morocco, often called the "Grandmother of Marrakech" for its similar architecture and traditional markets.
  • 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_69d6aa9983c08190b0ef61603b69feac completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d7992164d88190a01ed567b2529227 completed April 9, 2026, 12:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3e76a49fc8190945f4770bf808b43 completed April 18, 2026, 8:19 p.m.
Created at: April 8, 2026, 9:26 p.m.