Triple
T34658870
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | União do Vegetal |
E890052
|
entity |
| Predicate | legalStatusInBrazil |
P155001
|
FINISHED |
| Object | recognized religious use of ayahuasca |
—
|
LITERAL 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: recognized religious use of ayahuasca | Statement: [União do Vegetal, legalStatusInBrazil, recognized religious use of ayahuasca]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalStatusInBrazil Context triple: [União do Vegetal, legalStatusInBrazil, recognized religious use of ayahuasca]
-
A.
statusInBrazil
chosen
Indicates the legal, social, or operational status that an entity holds specifically within the context of Brazil.
-
B.
legalStatusInPortugal
Indicates the legal status or classification that an entity holds specifically within the jurisdiction of Portugal.
-
C.
BrazilNeededResult
Indicates that Brazil required or depended on a particular outcome or result to occur or be achieved.
-
D.
legalStatusAccordingToIndia
Indicates the legal status or classification of an entity as defined specifically by the laws and regulations of India.
-
E.
roleInBrazil
Indicates that an entity holds or held a specific role, position, or function within the context of Brazil.
- F. None of above.
Provenance (3 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_69f349d906bc8190b2efd9eff237d94b |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69ff6fba1a5c8190a660279a6271d785 |
completed | May 9, 2026, 5:32 p.m. |
| PD | Predicate disambiguation | batch_69ff6f59388c8190a7d6ab7bc7705bc0 |
completed | May 9, 2026, 5:31 p.m. |
Created at: May 1, 2026, 2:04 a.m.