Triple

T650472
Position Surface form Disambiguated ID Type / Status
Subject Guernica E11334 entity
Predicate twinnedWith P1072 FINISHED
Object Rentería
Rentería (Errenteria) is a town in the Basque province of Gipuzkoa in northern Spain, known for its industrial history and proximity to San Sebastián.
 E81781 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: Rentería | Statement: [Guernica, twinnedWith, Rentería]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Rentería
Context triple: [Guernica, twinnedWith, Rentería]
  • A. El Caminero
    El Caminero is a major southern terminal station of Mexico City’s Metrobús bus rapid transit system.
  • B. Spínola
    Spínola is a Portuguese surname most prominently associated with António de Spínola, a key military figure and political leader during Portugal’s Carnation Revolution.
  • C. Boyeros
    Boyeros is a municipality in Havana, Cuba, known for hosting the country’s main international gateway, José Martí International Airport.
  • D. Illapel
    Illapel is a city in Chile's Coquimbo Region known as an administrative and commercial center in the Choapa Valley.
  • E. La Frutera
    La Frutera is a historical nickname for the United Fruit Company, the powerful U.S.-based banana and agricultural conglomerate that dominated much of Central American trade and politics in the 20th century.
  • 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: Rentería
Triple: [Guernica, twinnedWith, Rentería]
Generated description
Rentería (Errenteria) is a town in the Basque province of Gipuzkoa in northern Spain, known for its industrial history and proximity to San Sebastián.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Rentería
Target entity description: Rentería (Errenteria) is a town in the Basque province of Gipuzkoa in northern Spain, known for its industrial history and proximity to San Sebastián.
  • A. El Caminero
    El Caminero is a major southern terminal station of Mexico City’s Metrobús bus rapid transit system.
  • B. Spínola
    Spínola is a Portuguese surname most prominently associated with António de Spínola, a key military figure and political leader during Portugal’s Carnation Revolution.
  • C. Boyeros
    Boyeros is a municipality in Havana, Cuba, known for hosting the country’s main international gateway, José Martí International Airport.
  • D. Illapel
    Illapel is a city in Chile's Coquimbo Region known as an administrative and commercial center in the Choapa Valley.
  • E. La Frutera
    La Frutera is a historical nickname for the United Fruit Company, the powerful U.S.-based banana and agricultural conglomerate that dominated much of Central American trade and politics in the 20th century.
  • 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_69a493266a2881909daf4c40f719dee8 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49f33b6d881908b6662b73d6fe833 completed March 1, 2026, 8:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69a59145fbbc8190b60b8bc420a3643c completed March 2, 2026, 1:31 p.m.
NEDg Description generation batch_69a591c8d1c48190b0f2313662ca9a5d completed March 2, 2026, 1:34 p.m.
NED2 Entity disambiguation (via description) batch_69a59273382881909c51d367286c9f7c completed March 2, 2026, 1:36 p.m.
Created at: March 1, 2026, 7:36 p.m.