Triple
T5768642
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ecosia |
E127272
|
entity |
| Predicate | donatesPercentageOfProfits |
P53474
|
FINISHED |
| Object | at least 80% |
—
|
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: at least 80% | Statement: [Ecosia, donatesPercentageOfProfits, at least 80%]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: donatesPercentageOfProfits Context triple: [Ecosia, donatesPercentageOfProfits, at least 80%]
-
A.
profitsFrom
Indicates that one entity gains financial or material benefit as a result of another entity’s actions, existence, or situation.
-
B.
philanthropicDonation
Indicates that one entity voluntarily gives money, goods, or services to another entity for charitable or public-benefit purposes without expecting direct compensation.
-
C.
tributeMethod
Indicates the means or manner by which tribute is given, delivered, or fulfilled between parties.
-
D.
donated
Indicates that one entity voluntarily gave something of value (such as money, goods, or time) to another entity, typically without expecting anything in return.
-
E.
donationStyle
chosen
Indicates the characteristic manner or approach with which a donation is given or carried out.
- 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_69c00834f6308190851b0abeddd8ed7e |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c02acb12c081908e4beee4a957f9f9 |
completed | March 22, 2026, 5:45 p.m. |
| PD | Predicate disambiguation | batch_69c021ce8d3c81909b332cb1c33a61ad |
completed | March 22, 2026, 5:07 p.m. |
Created at: March 22, 2026, 3:49 p.m.