Triple

T13345435
Position Surface form Disambiguated ID Type / Status
Subject Segrià E317936 entity
Predicate borders P224 FINISHED
Object La Llitera
La Llitera is a comarca (county) in the province of Huesca, in the autonomous community of Aragon, northeastern Spain.
 E1035654 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 Llitera | Statement: [Segrià, borders, La Llitera]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: La Llitera
Context triple: [Segrià, borders, La Llitera]
  • A. Casa del Libro
    Casa del Libro is a major Spanish bookstore chain and literary retailer known for its extensive selection of books and cultural events.
  • B. Casa del Libro
    Casa del Libro is a small museum and cultural institution in Old San Juan, Puerto Rico, renowned for its rare books, historic manuscripts, and exhibitions on the art of the book.
  • C. Casa Calvet
    Casa Calvet is a Barcelona residential building designed by Antoni Gaudí that blends Baroque-inspired ornamentation with early modernist architectural elements.
  • D. Biblioteca de Catalunya
    The Biblioteca de Catalunya is the national library of Catalonia, housing and preserving the region’s bibliographic heritage in Barcelona.
  • E. La Sagrera
    La Sagrera is a major multimodal transport hub in Barcelona that serves as an interchange between several metro lines, commuter trains, and future high-speed rail services.
  • 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 Llitera
Triple: [Segrià, borders, La Llitera]
Generated description
La Llitera is a comarca (county) in the province of Huesca, in the autonomous community of Aragon, northeastern Spain.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: La Llitera
Target entity description: La Llitera is a comarca (county) in the province of Huesca, in the autonomous community of Aragon, northeastern Spain.
  • A. Casa del Libro
    Casa del Libro is a small museum and cultural institution in Old San Juan, Puerto Rico, renowned for its rare books, historic manuscripts, and exhibitions on the art of the book.
  • B. Casa del Libro
    Casa del Libro is a major Spanish bookstore chain and literary retailer known for its extensive selection of books and cultural events.
  • C. Casa Calvet
    Casa Calvet is a Barcelona residential building designed by Antoni Gaudí that blends Baroque-inspired ornamentation with early modernist architectural elements.
  • D. Biblioteca de Catalunya
    The Biblioteca de Catalunya is the national library of Catalonia, housing and preserving the region’s bibliographic heritage in Barcelona.
  • E. La Sagrera
    La Sagrera is a major multimodal transport hub in Barcelona that serves as an interchange between several metro lines, commuter trains, and future high-speed rail services.
  • 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_69d806b5a3c08190b42c267fb092f98a completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d99e89c65c819093f3bea11d6073c5 completed April 11, 2026, 1:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69f71f439b3c8190b35fd4d097d65068 completed May 3, 2026, 10:11 a.m.
NEDg Description generation batch_69f7204ac36c8190a04e921442489e9c completed May 3, 2026, 10:15 a.m.
NED2 Entity disambiguation (via description) batch_69f7221887208190ac98945a023bc496 completed May 3, 2026, 10:23 a.m.
Created at: April 9, 2026, 9:31 p.m.