Triple

T24350118
Position Surface form Disambiguated ID Type / Status
Subject Southern Lithuania E613761 entity
Predicate hasProtectedArea P855 FINISHED
Object Veisiejai Regional Park
Veisiejai Regional Park is a protected natural and cultural landscape in southern Lithuania, known for its lakes, forests, and traditional rural scenery.
E1633354 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: Veisiejai Regional Park | Statement: [Southern Lithuania, hasProtectedArea, Veisiejai Regional Park]
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: Veisiejai Regional Park
Triple: [Southern Lithuania, hasProtectedArea, Veisiejai Regional Park]
Generated description
Veisiejai Regional Park is a protected natural and cultural landscape in southern Lithuania, known for its lakes, forests, and traditional rural scenery.

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_69e2d7ddd29481909e7f539a6072bd71 completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f29344061081908ffcb85787f334a2 completed April 29, 2026, 11:24 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0fe3535d308190b4709c86a7225287 completed May 22, 2026, 5:02 a.m.
NEDg Description generation batch_6a0fe41d67308190be8f1977f1cd2782 completed May 22, 2026, 5:05 a.m.
NED2 Entity disambiguation (via description) batch_6a0fe498856c8190bd35cc957266b936 completed May 22, 2026, 5:07 a.m.
Created at: April 18, 2026, 1:59 a.m.