Triple

T35948569
Position Surface form Disambiguated ID Type / Status
Subject Nylandtia spinosa E1039653 entity
Predicate taxonomicSynonymOf P97460 FINISHED
Object Muraltia spinosa
Muraltia spinosa is a spiny, evergreen shrub native to South Africa, known for its small purple flowers and edible red berries, and commonly used in traditional medicine and coastal landscaping.
E2163362 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: Muraltia spinosa | Statement: [Nylandtia spinosa, taxonomicSynonymOf, Muraltia spinosa]
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: Muraltia spinosa
Triple: [Nylandtia spinosa, taxonomicSynonymOf, Muraltia spinosa]
Generated description
Muraltia spinosa is a spiny, evergreen shrub native to South Africa, known for its small purple flowers and edible red berries, and commonly used in traditional medicine and coastal landscaping.

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_69f76e25ea488190b7cee970b3e70382 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7abd451208190b5894430bbd23863 completed May 3, 2026, 8:11 p.m.
NED1 Entity disambiguation (via context triple) batch_6a38b6fdd01481909a20d247c5ba542a completed June 22, 2026, 4:15 a.m.
NEDg Description generation batch_6a38b85bf4908190aecf55c46230322b completed June 22, 2026, 4:21 a.m.
NED2 Entity disambiguation (via description) batch_6a38b8dae4ac8190a020a5e984acef6b completed June 22, 2026, 4:23 a.m.
Created at: May 3, 2026, 4:07 p.m.