Triple

T9290515
Position Surface form Disambiguated ID Type / Status
Subject Costa Brava E223503 entity
Predicate contains P35 FINISHED
Object Begur
Begur is a picturesque coastal town in Catalonia, Spain, known for its medieval hilltop castle, charming old quarter, and scenic beaches along the Costa Brava.
 E813645 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: Begur | Statement: [Costa Brava, contains, Begur]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Begur
Context triple: [Costa Brava, contains, Begur]
  • A. Banyoles
    Banyoles is a town in Catalonia, Spain, best known for its large natural lake and scenic surroundings.
  • B. Tàrrega
    Tàrrega is a historic town in Catalonia, Spain, known for its cultural festivals and medieval heritage.
  • C. Cerdanya
    Cerdanya is a historic region in the eastern Pyrenees, now divided between France and Spain, known for its mountainous landscapes and Catalan cultural heritage.
  • D. Ampurias
    Ampurias (Empúries) was an ancient Greek and later Roman coastal settlement in northeastern Spain that became an important trading hub in the western Mediterranean.
  • E. Manresa
    Manresa is a historic city in Catalonia, Spain, known for its medieval architecture and significance as a religious and commercial center in the region.
  • 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: Begur
Triple: [Costa Brava, contains, Begur]
Generated description
Begur is a picturesque coastal town in Catalonia, Spain, known for its medieval hilltop castle, charming old quarter, and scenic beaches along the Costa Brava.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Begur
Target entity description: Begur is a picturesque coastal town in Catalonia, Spain, known for its medieval hilltop castle, charming old quarter, and scenic beaches along the Costa Brava.
  • A. Banyoles
    Banyoles is a town in Catalonia, Spain, best known for its large natural lake and scenic surroundings.
  • B. Tàrrega
    Tàrrega is a historic town in Catalonia, Spain, known for its cultural festivals and medieval heritage.
  • C. Cerdanya
    Cerdanya is a historic region in the eastern Pyrenees, now divided between France and Spain, known for its mountainous landscapes and Catalan cultural heritage.
  • D. Ampurias
    Ampurias (Empúries) was an ancient Greek and later Roman coastal settlement in northeastern Spain that became an important trading hub in the western Mediterranean.
  • E. Manresa
    Manresa is a historic city in Catalonia, Spain, known for its medieval architecture and significance as a religious and commercial center in the region.
  • 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_69ca8422ddf881908a3f8f876c9f53aa completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd0865a7108190b807afd259980db2 completed April 1, 2026, 11:58 a.m.
NED1 Entity disambiguation (via context triple) batch_69d189e115d8819092c3ecbeec8b450f completed April 4, 2026, 10 p.m.
NEDg Description generation batch_69d18aa1e9a48190bf26da5482fd0770 completed April 4, 2026, 10:03 p.m.
NED2 Entity disambiguation (via description) batch_69d18b6a2fb0819092ee274310721b50 completed April 4, 2026, 10:06 p.m.
Created at: March 30, 2026, 7:35 p.m.