Triple
T10435037
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brazil–United States relations |
E246014
|
entity |
| Predicate | peopleToPeopleLinks |
P83090
|
FINISHED |
| Object | tourism |
—
|
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: tourism | Statement: [Brazil–United States relations, peopleToPeopleLinks, tourism]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: peopleToPeopleLinks Context triple: [Brazil–United States relations, peopleToPeopleLinks, tourism]
-
A.
peopleAssociatedWith
Indicates that there exists some form of connection, involvement, or relationship between the referenced people.
-
B.
organizationAssociatedWith
Indicates that there is a formal or recognized connection or affiliation between an organization and another entity.
-
C.
politicalEntityAssociated
Indicates that one entity has a political connection, affiliation, or involvement with another entity, such as through membership, support, representation, or influence.
-
D.
associationWithHumans
chosen
Indicates a general relationship, connection, or involvement between an entity and one or more humans.
-
E.
partnerInOrganizationWith
Indicates that two entities are associated as partners within the same organization or organizational context.
- 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_69d381bf3dc08190bf35a2643e4e8f22 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4ea67344c81909984ab99b0ef1e64 |
completed | April 7, 2026, 11:28 a.m. |
| PD | Predicate disambiguation | batch_69d4dfbc546881908f312c66ee195f79 |
completed | April 7, 2026, 10:43 a.m. |
Created at: April 6, 2026, 12:14 p.m.