Triple
T29791975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Northern Lights |
E756432
|
entity |
| Predicate | benefitEvent |
P175358
|
FINISHED |
| Object | Ethiopian famine relief |
—
|
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: Ethiopian famine relief | Statement: [Northern Lights, benefitEvent, Ethiopian famine relief]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: benefitEvent Context triple: [Northern Lights, benefitEvent, Ethiopian famine relief]
-
A.
benefitedFromEvent
Indicates that an entity gained an advantage, improvement, or positive outcome as a result of a particular event.
-
B.
beneficeType
Indicates the specific category or kind of benefice (ecclesiastical office or endowed church position) associated with an entity.
-
C.
benefitAvailableAt
Indicates that a particular benefit can be obtained, accessed, or used at a specified location, time, or context.
-
D.
benefice
Indicates that one entity grants or bestows a benefit, favor, or advantage upon another.
-
E.
benefitsCause
Indicates that one entity gains an advantage, improvement, or positive outcome as a result of another entity or cause.
- F. None of above. chosen
Provenance (4 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_69f22454583081908927516cb9938d1d |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f6d1d916f881909575c2b22c416a5b |
completed | May 3, 2026, 4:40 a.m. |
| PD | Predicate disambiguation | batch_69f6cfe2183481908ae4e85a59c66f69 |
completed | May 3, 2026, 4:32 a.m. |
| PDg | Predicate description generation | batch_69f6d0d331dc8190be5aa6bfc6365e67 |
completed | May 3, 2026, 4:36 a.m. |
Created at: April 29, 2026, 5:13 p.m.