Triple

T29512458
Position Surface form Disambiguated ID Type / Status
Subject Ilha do Mel E748691 entity
Predicate partOf P40 FINISHED
Object Ilha do Mel State Park
Ilha do Mel State Park is a protected natural area in Paraná, Brazil, known for its preserved Atlantic Forest, scenic beaches, and ecotourism opportunities on Ilha do Mel.
E1872210 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: Ilha do Mel State Park | Statement: [Ilha do Mel, partOf, Ilha do Mel State 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: Ilha do Mel State Park
Triple: [Ilha do Mel, partOf, Ilha do Mel State Park]
Generated description
Ilha do Mel State Park is a protected natural area in Paraná, Brazil, known for its preserved Atlantic Forest, scenic beaches, and ecotourism opportunities on Ilha do Mel.

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_69f0bd461c208190bec20bbf24e02cc5 completed April 28, 2026, 1:59 p.m.
NER Named-entity recognition batch_69f66c60b6ec81908d373511b5ce4f1b completed May 2, 2026, 9:28 p.m.
NED1 Entity disambiguation (via context triple) batch_6a260c1f07508190befee1e371277baa completed June 8, 2026, 12:26 a.m.
NEDg Description generation batch_6a2611ef35248190b7379f0f8cff1c4b completed June 8, 2026, 12:50 a.m.
NED2 Entity disambiguation (via description) batch_6a26165546a08190b0f1d51c1881a655 completed June 8, 2026, 1:09 a.m.
Created at: April 28, 2026, 4:33 p.m.