Triple
T33260859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cerro Porteño vs Olimpia |
E851507
|
entity |
| Predicate | hasTeam1FullName |
P8312
|
FINISHED |
| Object | Club Cerro Porteño |
—
|
NE NERFINISHED |
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: Club Cerro Porteño | Statement: [Cerro Porteño vs Olimpia, hasTeam1FullName, Club Cerro Porteño]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTeam1FullName Context triple: [Cerro Porteño vs Olimpia, hasTeam1FullName, Club Cerro Porteño]
-
A.
team1FullName
chosen
Indicates the complete official name associated with the first team in a given context or comparison.
-
B.
stateTeam1
Indicates that the first team is associated with, represents, or belongs to a particular state.
-
C.
regionTeam1
Indicates that the first team is associated with, belongs to, or represents a specific region.
-
D.
homeTeamRunnerUp
Indicates that the referenced team finished in second place (runner-up) in a competition held at its home venue or location.
-
E.
team2FullName
Indicates the complete official name of the second team involved in a context or comparison.
- 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_69f34963135c819084e7f1d483421f00 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fd7e364a648190a1e9e1d9fc76e99e |
completed | May 8, 2026, 6:09 a.m. |
| PD | Predicate disambiguation | batch_69fd7bb547608190a3b04dddbca6b8bc |
completed | May 8, 2026, 5:59 a.m. |
Created at: May 1, 2026, 1:31 a.m.