Triple

T562105
Position Surface form Disambiguated ID Type / Status
Subject Beni Suef Governorate E13473 entity
Predicate hasCity P316 FINISHED
Object Beba
Beba is a city in Egypt’s Beni Suef Governorate, known as a local administrative and commercial center in the region.
 E78562 NE FINISHED

How this triple was built (4 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: Beba | Statement: [Beni Suef Governorate, hasCity, Beba]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Beba
Context triple: [Beni Suef Governorate, hasCity, Beba]
  • A. Niña
    Niña was one of the three ships in Christopher Columbus’s 1492 voyage across the Atlantic, notable for its role in the first European expedition to the Americas.
  • B. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • C. Gabi
    Gabi is a common diminutive form of the given name Gabriel (and sometimes Gabriela), used in various languages as a familiar or affectionate nickname.
  • D. Rebeca
    Rebeca is a feminine given name, commonly used in Spanish- and Portuguese-speaking countries, that is a variant of the name Rebecca.
  • E. Paola
    Paola is an Italian noblewoman who became Queen consort of Belgium as the wife of King Albert II.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Beba
Triple: [Beni Suef Governorate, hasCity, Beba]
Generated description
Beba is a city in Egypt’s Beni Suef Governorate, known as a local administrative and commercial center in the region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Beba
Target entity description: Beba is a city in Egypt’s Beni Suef Governorate, known as a local administrative and commercial center in the region.
  • A. Niña
    Niña was one of the three ships in Christopher Columbus’s 1492 voyage across the Atlantic, notable for its role in the first European expedition to the Americas.
  • B. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • C. Gabi
    Gabi is a common diminutive form of the given name Gabriel (and sometimes Gabriela), used in various languages as a familiar or affectionate nickname.
  • D. Rebeca
    Rebeca is a feminine given name, commonly used in Spanish- and Portuguese-speaking countries, that is a variant of the name Rebecca.
  • E. Paola
    Paola is an Italian noblewoman who became Queen consort of Belgium as the wife of King Albert II.
  • F. None of above. chosen

Provenance (5 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_69a4933edcf08190b35ecfd6014caee6 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49a700e608190b235246df057bd9b completed March 1, 2026, 7:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69a56930dfd88190a991adafc406c5ac completed March 2, 2026, 10:40 a.m.
NEDg Description generation batch_69a569bac3f8819083b153b73caf31c2 completed March 2, 2026, 10:43 a.m.
NED2 Entity disambiguation (via description) batch_69a56a51e1fc8190aa00945fff740927 completed March 2, 2026, 10:45 a.m.
Created at: March 1, 2026, 7:32 p.m.