Triple

T35869006
Position Surface form Disambiguated ID Type / Status
Subject Madeira archipelago urban network E1037169 entity
Predicate hasPart P35 FINISHED
Object Porto Moniz
Porto Moniz is a coastal municipality on the northwestern tip of Madeira Island, Portugal, known for its dramatic volcanic landscapes and natural lava rock swimming pools.
E2161943 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: Porto Moniz | Statement: [Madeira archipelago urban network, hasPart, Porto Moniz]
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: Porto Moniz
Triple: [Madeira archipelago urban network, hasPart, Porto Moniz]
Generated description
Porto Moniz is a coastal municipality on the northwestern tip of Madeira Island, Portugal, known for its dramatic volcanic landscapes and natural lava rock swimming pools.

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_69f76e1d279c8190843e5b64a0a12c3f completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7a9c881ac8190baeebe3d7f69d0f6 completed May 3, 2026, 8:02 p.m.
NED1 Entity disambiguation (via context triple) batch_6a38ae1d6ab081909b312b1e961714f8 completed June 22, 2026, 3:38 a.m.
NEDg Description generation batch_6a38b056e91c81908896809d286b4b42 completed June 22, 2026, 3:47 a.m.
NED2 Entity disambiguation (via description) batch_6a38b0a4372c8190bb7cccc31a4a571e completed June 22, 2026, 3:48 a.m.
Created at: May 3, 2026, 4:06 p.m.