Triple

T28182754
Position Surface form Disambiguated ID Type / Status
Subject House of Nava-Grimón E716079 entity
Predicate hasTerritorialBase P21614 FINISHED
Object city of La Laguna
The city of La Laguna is a historic urban center on the island of Tenerife in Spain’s Canary Islands, renowned for its well-preserved colonial architecture and status as a UNESCO World Heritage Site.
E1805676 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: city of La Laguna | Statement: [House of Nava-Grimón, hasTerritorialBase, city of La Laguna]
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: city of La Laguna
Triple: [House of Nava-Grimón, hasTerritorialBase, city of La Laguna]
Generated description
The city of La Laguna is a historic urban center on the island of Tenerife in Spain’s Canary Islands, renowned for its well-preserved colonial architecture and status as a UNESCO World Heritage Site.

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_69efd6b4fc5c81909dd88f01a8c2b35d completed April 27, 2026, 9:35 p.m.
NER Named-entity recognition batch_69f6428444988190b65975dcabae95eb completed May 2, 2026, 6:29 p.m.
NED1 Entity disambiguation (via context triple) batch_6a15d7c90468819095f6f789ea6b3bc0 completed May 26, 2026, 5:26 p.m.
NEDg Description generation batch_6a15dbaf1224819090a0ec3d323a8601 completed May 26, 2026, 5:43 p.m.
NED2 Entity disambiguation (via description) batch_6a15dc178a708190927987ae171664eb completed May 26, 2026, 5:44 p.m.
Created at: April 27, 2026, 10:20 p.m.