Triple

T20322697
Position Surface form Disambiguated ID Type / Status
Subject Taiz Governorate E492246 entity
Predicate containsCity P294 FINISHED
Object Taiz 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: Taiz | Statement: [Taiz Governorate, containsCity, Taiz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Taiz
Context triple: [Taiz Governorate, containsCity, 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. Qairawan
    Qairawan is a locality within Kuwait’s Al Asimah (Capital) Governorate, forming part of the urban area around Kuwait City.
  • D. Berrechid
    Berrechid is a rapidly growing city in northwestern Morocco known as an important agricultural and industrial hub within the Casablanca-Settat region.
  • E. 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.
  • 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_69e0b4a0134081909113563e1c3ba68a completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6778d95dc81909b1c87d26b5d3a33 completed April 20, 2026, 6:59 p.m.
Created at: April 16, 2026, 11:20 a.m.