Triple
T35944576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sani Abacha |
E1039552
|
entity |
| Predicate | allegedToHaveStolen |
P48033
|
FINISHED |
| Object | billions of US dollars from Nigerian state funds |
—
|
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: billions of US dollars from Nigerian state funds | Statement: [Sani Abacha, allegedToHaveStolen, billions of US dollars from Nigerian state funds]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: allegedToHaveStolen Context triple: [Sani Abacha, allegedToHaveStolen, billions of US dollars from Nigerian state funds]
-
A.
stolenBy
chosen
Indicates that something has been taken unlawfully or without permission by a particular entity.
-
B.
stolenDuring
Indicates that one entity was stolen in the course of, or at the time of, another specified event or time period.
-
C.
stolenFrom
Indicates that something was taken unlawfully from a particular source or owner.
-
D.
stolenItems
Indicates that certain items have been taken without permission or legal right, typically as the result of theft.
-
E.
notableTheft
Indicates that an entity is involved in a theft event that is widely recognized or significant in some notable way.
- 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_69f76e24bbd0819096b837d35371639a |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fcc7338120819081cb46547d60f2cb |
completed | May 7, 2026, 5:09 p.m. |
| PD | Predicate disambiguation | batch_69fcc58566a0819082d5ea36e03bf0c6 |
completed | May 7, 2026, 5:01 p.m. |
Created at: May 3, 2026, 4:07 p.m.