Triple

T27060080
Position Surface form Disambiguated ID Type / Status
Subject Flag of Candelaria E685017 entity
Predicate jurisdiction P82 FINISHED
Object Candelaria
Candelaria is a municipality in the Canary Islands of Spain, located on the island of Tenerife and known as an important Catholic pilgrimage site dedicated to the Virgin of Candelaria.
E175350 NE FINISHED

How this triple was built (2 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: Candelaria | Statement: [Flag of Candelaria, jurisdiction, Candelaria]
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: Candelaria
Triple: [Flag of Candelaria, jurisdiction, Candelaria]
Generated description
Candelaria is a municipality in the Canary Islands of Spain, located on the island of Tenerife and known as an important Catholic pilgrimage site dedicated to the Virgin of Candelaria.

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_69ef14835fcc81908bd737b4267ae528 completed April 27, 2026, 7:47 a.m.
NER Named-entity recognition batch_69f622e3ab7081909692e4857e7d7633 completed May 2, 2026, 4:14 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12a7b7a938819092f1014987f0f66c completed May 24, 2026, 7:24 a.m.
NEDg Description generation batch_6a12aa2ba1b88190aa2dde20326bdf24 completed May 24, 2026, 7:35 a.m.
NED2 Entity disambiguation (via description) batch_6a12aa9670988190be61c9aaa57b70c9 completed May 24, 2026, 7:36 a.m.
Created at: April 27, 2026, 8:20 a.m.