Triple
T21471363
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2015 FIFA corruption case |
E529738
|
entity |
| Predicate | numberOfPeopleChargedBy2017 |
P35608
|
FINISHED |
| Object | over 40 |
—
|
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: over 40 | Statement: [2015 FIFA corruption case, numberOfPeopleChargedBy2017, over 40]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfPeopleChargedBy2017 Context triple: [2015 FIFA corruption case, numberOfPeopleChargedBy2017, over 40]
-
A.
numberOfPeopleAccused
Indicates the count of individuals who are formally alleged to have committed a particular act or offense.
-
B.
numberOfPeoplePunished
Indicates the count of individuals who received punishment in relation to a specified event, action, or context.
-
C.
numberOfIndictedPersonsApproximate
chosen
Indicates an approximate count of persons who have been formally indicted in a given context or case.
-
D.
numberOfArrests
Indicates the count of times an entity has been arrested.
-
E.
guiltyPleaCount
Indicates the number of times an entity has entered a plea of guilty in legal proceedings.
- 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_69e0c459acb481909bb6ee452a0045c7 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9ea13adfc819093324ae6fe66c3fd |
completed | April 23, 2026, 9:44 a.m. |
| PD | Predicate disambiguation | batch_69e631ec1d048190b6da97da8222e413 |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:18 p.m.