Triple
T21756663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rio Protocol |
E537057
|
entity |
| Predicate | effectOnEcuador |
P8692
|
FINISHED |
| Object | Ecuador ceded claims to large areas of the Amazon region |
—
|
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: Ecuador ceded claims to large areas of the Amazon region | Statement: [Rio Protocol, effectOnEcuador, Ecuador ceded claims to large areas of the Amazon region]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnEcuador Context triple: [Rio Protocol, effectOnEcuador, Ecuador ceded claims to large areas of the Amazon region]
-
A.
statusInEcuador
Indicates the legal, social, or official condition or standing that an entity has within the context of Ecuador.
-
B.
effectOnUnitedStates
Indicates the impact, influence, or consequences that something has on the United States.
-
C.
effectOnSpain
Indicates a relationship where one entity produces an influence, change, or consequence specifically affecting Spain.
-
D.
effectOnRussia
Indicates the impact or consequences that something has on Russia.
-
E.
affectedCountry
chosen
Indicates that a particular country is impacted or influenced by an event, action, or condition.
- 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_69e0c46eab808190b848242d63a17c47 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f01d8ea04c8190a8c4fa43b3f23935 |
completed | April 28, 2026, 2:38 a.m. |
| PD | Predicate disambiguation | batch_69e6969e46088190b13d6e9025775ea3 |
completed | April 20, 2026, 9:11 p.m. |
Created at: April 16, 2026, 6:50 p.m.