Triple

T13574658
Position Surface form Disambiguated ID Type / Status
Subject Hakitia E324250 entity
Predicate alternativeName P39 FINISHED
Object Haquetía
Haquetía is a Judeo-Spanish dialect historically spoken by North African Sephardic Jewish communities, particularly in northern Morocco and Gibraltar.
 E1049771 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: Haquetía | Statement: [Hakitia, alternativeName, Haquetía]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Haquetía
Context triple: [Hakitia, alternativeName, Haquetía]
  • A. Tasqueña
    Tasqueña is a major transit hub and southern terminus of Mexico City’s Metro Line 2, integrating metro, light rail, and bus services.
  • B. Frasqueira
    Frasqueira is a premium category of Madeira wine denoting long-aged, high-quality vintage bottlings.
  • C. Azaña
    Azaña is the surname of Manuel Azaña, a prominent Spanish politician and writer who served as President of the Second Spanish Republic.
  • D. Güemes
    Güemes is a Spanish surname most notably associated with Argentine independence leader Martín Miguel de Güemes.
  • E. Balazote
    Balazote is a municipality in the province of Albacete, Spain, known for its archaeological heritage and rural Castilian-La Mancha setting.
  • 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: Haquetía
Triple: [Hakitia, alternativeName, Haquetía]
Generated description
Haquetía is a Judeo-Spanish dialect historically spoken by North African Sephardic Jewish communities, particularly in northern Morocco and Gibraltar.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Haquetía
Target entity description: Haquetía is a Judeo-Spanish dialect historically spoken by North African Sephardic Jewish communities, particularly in northern Morocco and Gibraltar.
  • A. Tasqueña
    Tasqueña is a major transit hub and southern terminus of Mexico City’s Metro Line 2, integrating metro, light rail, and bus services.
  • B. Frasqueira
    Frasqueira is a premium category of Madeira wine denoting long-aged, high-quality vintage bottlings.
  • C. Azaña
    Azaña is the surname of Manuel Azaña, a prominent Spanish politician and writer who served as President of the Second Spanish Republic.
  • D. Güemes
    Güemes is a Spanish surname most notably associated with Argentine independence leader Martín Miguel de Güemes.
  • E. Balazote
    Balazote is a municipality in the province of Albacete, Spain, known for its archaeological heritage and rural Castilian-La Mancha setting.
  • 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_69d80769100c819099111274614f5ed2 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbb02b1f108190a12af382d1de70bb completed April 12, 2026, 2:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69f76bba21f88190b8952fb0879e623d completed May 3, 2026, 3:37 p.m.
NEDg Description generation batch_69f77641e5308190a75bcffeb9bfd7b4 completed May 3, 2026, 4:22 p.m.
NED2 Entity disambiguation (via description) batch_69f779178dc48190bb0de790de30d8b0 completed May 3, 2026, 4:34 p.m.
Created at: April 9, 2026, 9:48 p.m.