Triple
T32092666
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Argentina defeated France in opening match |
E819636
|
entity |
| Predicate | teamCoachOfArgentina |
P173531
|
FINISHED |
| Object | Marcelo Loffreda |
—
|
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: Marcelo Loffreda | Statement: [Argentina defeated France in opening match, teamCoachOfArgentina, Marcelo Loffreda]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: teamCoachOfArgentina Context triple: [Argentina defeated France in opening match, teamCoachOfArgentina, Marcelo Loffreda]
-
A.
ArgentinaCaptain
Indicates that a person serves as the captain of Argentina’s national team in a particular sport or context.
-
B.
coachOfItaly
Indicates that one entity serves as the coach of the Italian national team.
-
C.
ArgentinaGoals
Indicates the number of goals scored by Argentina in a given match or context.
-
D.
spainCoach
Indicates that an entity serves as the coach of a Spain-related sports team or delegation.
-
E.
atleticoMadridCoach
Indicates that one entity serves as the coach or manager of the Atlético Madrid football team.
- 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_69f349004b2481908ce2e50af0d579a8 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6b63edd4c819093b664b38a69d7c8 |
completed | May 3, 2026, 2:43 a.m. |
| PD | Predicate disambiguation | batch_69f6b3a7bdb481908d16a32f49e38c2c |
completed | May 3, 2026, 2:32 a.m. |
| PDg | Predicate description generation | batch_69f6b49339048190b617a6749f648825 |
completed | May 3, 2026, 2:36 a.m. |
Created at: May 1, 2026, 12:25 a.m.