Triple

T3796146
Position Surface form Disambiguated ID Type / Status
Subject Ribera d’Ebre E89774 entity
Predicate containsSettlement P847 FINISHED
Object Tivissa
Tivissa is a historic village in Catalonia, Spain, known for its scenic setting among the mountains of the Ribera d’Ebre region and its well-preserved medieval core.
E389591 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: Tivissa | Statement: [Ribera d’Ebre, containsSettlement, Tivissa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tivissa
Context triple: [Ribera d’Ebre, containsSettlement, Tivissa]
  • A. Odda
    Odda is a town in western Norway known for its dramatic fjord landscape, industrial heritage, and proximity to popular hiking destinations like Trolltunga.
  • B. Vidzeme
    Vidzeme is a historical region in northern Latvia known for its rich cultural heritage, forests, and role in the development of Latvian national identity.
  • C. Goytre
    Goytre is a village and community located within the county borough of Neath Port Talbot in South Wales.
  • D. Mora
    Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
  • E. Mora
    Mora is a town in central Sweden’s Dalarna region, known for its traditional Swedish culture, proximity to Lake Siljan, and as the finish line of the Vasaloppet cross-country ski race.
  • 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: Tivissa
Triple: [Ribera d’Ebre, containsSettlement, Tivissa]
Generated description
Tivissa is a historic village in Catalonia, Spain, known for its scenic setting among the mountains of the Ribera d’Ebre region and its well-preserved medieval core.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Tivissa
Target entity description: Tivissa is a historic village in Catalonia, Spain, known for its scenic setting among the mountains of the Ribera d’Ebre region and its well-preserved medieval core.
  • A. Odda
    Odda is a town in western Norway known for its dramatic fjord landscape, industrial heritage, and proximity to popular hiking destinations like Trolltunga.
  • B. Vidzeme
    Vidzeme is a historical region in northern Latvia known for its rich cultural heritage, forests, and role in the development of Latvian national identity.
  • C. Goytre
    Goytre is a village and community located within the county borough of Neath Port Talbot in South Wales.
  • D. Mora
    Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
  • E. Mora
    Mora is a town in central Sweden’s Dalarna region, known for its traditional Swedish culture, proximity to Lake Siljan, and as the finish line of the Vasaloppet cross-country ski race.
  • 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_69aed9597d6881909b6ee3b9de859223 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aee79f09bc8190b7514a11a030eba5 completed March 9, 2026, 3:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4f05ea5e081908c4714ca35aed48b completed March 14, 2026, 5:21 a.m.
NEDg Description generation batch_69b4f2ed663c8190be431c7aae60259e completed March 14, 2026, 5:32 a.m.
NED2 Entity disambiguation (via description) batch_69b4f72eba988190acb96b44fc8b7c30 completed March 14, 2026, 5:50 a.m.
Created at: March 9, 2026, 3:15 p.m.