Triple

T8759738
Position Surface form Disambiguated ID Type / Status
Subject Cantabria E208164 entity
Predicate hasTraditionalRegion P14194 FINISHED
Object La Marina
La Marina is a traditional coastal region of Cantabria in northern Spain, known for its seaside towns, beaches, and maritime landscapes.
 E754679 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: La Marina | Statement: [Cantabria, hasTraditionalRegion, La Marina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: La Marina
Context triple: [Cantabria, hasTraditionalRegion, La Marina]
  • A. Baheri
    Baheri is a town located in the Darbhanga district of the Indian state of Bihar.
  • B. Flotta
    Flotta is a small island in the Orkney archipelago of Scotland, known for its oil terminal and role in North Sea petroleum operations.
  • C. Marín
    Marín is a coastal town in the province of Pontevedra, Galicia, Spain, known for its naval traditions and as a base of the Spanish Navy.
  • D. La Mar
    La Mar is an archaeological site in the western Maya lowlands known for its Classic-period Maya ruins and inscriptions.
  • E. Taganga
    Taganga is a small fishing village and popular backpacker destination on Colombia’s Caribbean coast, known for its beaches, diving, and proximity to Tayrona National Natural Park.
  • 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: La Marina
Triple: [Cantabria, hasTraditionalRegion, La Marina]
Generated description
La Marina is a traditional coastal region of Cantabria in northern Spain, known for its seaside towns, beaches, and maritime landscapes.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: La Marina
Target entity description: La Marina is a traditional coastal region of Cantabria in northern Spain, known for its seaside towns, beaches, and maritime landscapes.
  • A. Baheri
    Baheri is a town located in the Darbhanga district of the Indian state of Bihar.
  • B. Flotta
    Flotta is a small island in the Orkney archipelago of Scotland, known for its oil terminal and role in North Sea petroleum operations.
  • C. Marín
    Marín is a coastal town in the province of Pontevedra, Galicia, Spain, known for its naval traditions and as a base of the Spanish Navy.
  • D. La Mar
    La Mar is an archaeological site in the western Maya lowlands known for its Classic-period Maya ruins and inscriptions.
  • E. Taganga
    Taganga is a small fishing village and popular backpacker destination on Colombia’s Caribbean coast, known for its beaches, diving, and proximity to Tayrona National Natural Park.
  • 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_69ca835cd6b08190bd7c63db92f53c86 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5df81c58819089af99306e103dbb completed March 31, 2026, 11:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf434bcac08190be07175a26804185 completed April 3, 2026, 4:34 a.m.
NEDg Description generation batch_69cf452b237c8190958f7b42e9611e7b completed April 3, 2026, 4:42 a.m.
NED2 Entity disambiguation (via description) batch_69cf45e6f4108190ac6955264b466abb completed April 3, 2026, 4:45 a.m.
Created at: March 30, 2026, 6:40 p.m.