Triple
T28759145
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | football at the 1928 Summer Olympics |
E731757
|
entity |
| Predicate | featuredAfricanTeams |
P197731
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [football at the 1928 Summer Olympics, featuredAfricanTeams, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuredAfricanTeams Context triple: [football at the 1928 Summer Olympics, featuredAfricanTeams, true]
-
A.
numberOfQualifiedTeamsFromAfrica
Indicates the count of teams from Africa that meet the specified qualification criteria.
-
B.
featuredSouthAmericanTeams
Indicates that certain teams from South America were specially highlighted, showcased, or given prominence in a particular context or event.
-
C.
wonAfricaCupOfNations
Indicates that the subject has won the Africa Cup of Nations football tournament.
-
D.
featuredNationalTeams
Indicates that certain national teams are prominently highlighted or given special emphasis in a particular context or collection.
-
E.
AfricaCupOfNationsRunnersUp
Indicates that the subject finished as the runner-up (second place) in an edition of the Africa Cup of Nations tournament.
- 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_69f043ed68a881909e858a06bab7a247 |
completed | April 28, 2026, 5:21 a.m. |
| NER | Named-entity recognition | batch_69fea5e828cc8190a9b755a645dc56d2 |
completed | May 9, 2026, 3:11 a.m. |
| PD | Predicate disambiguation | batch_69fea36443f08190b2aced9b4a0525fd |
completed | May 9, 2026, 3 a.m. |
| PDg | Predicate description generation | batch_69fea5e75a5481908a04d91ee7255ebb |
completed | May 9, 2026, 3:11 a.m. |
Created at: April 28, 2026, 6:11 a.m.