Triple
T29888046
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Richarlison de Andrade |
E759068
|
entity |
| Predicate | hasJerseyNumberAtNationalTeam |
P156847
|
FINISHED |
| Object | 9 |
—
|
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: 9 | Statement: [Richarlison de Andrade, hasJerseyNumberAtNationalTeam, 9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasJerseyNumberAtNationalTeam Context triple: [Richarlison de Andrade, hasJerseyNumberAtNationalTeam, 9]
-
A.
playsForNationalTeamShirtNumber
chosen
Indicates the shirt number a player wears when representing their national team in official matches.
-
B.
jerseyNumber
Indicates the specific uniform number assigned to and worn by an individual, typically in a sports context.
-
C.
associatedWithNationalTeam
Indicates that an entity has a formal connection or involvement with a specific national team, such as playing for, coaching, or otherwise being officially linked to it.
-
D.
usedByNationalTeamSince
Indicates that something has been utilized or employed by a national team starting from a specific point in time.
-
E.
playsInNationalTeamAs
Indicates that an individual is a member of a national team and participates in it in a specific playing position or role.
- 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_69f2245de2f48190a481404896b56254 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f676fe75988190ae1bffb155a3c4e1 |
completed | May 2, 2026, 10:13 p.m. |
| PD | Predicate disambiguation | batch_69f66ec8298c8190b41fe9d182c05676 |
completed | May 2, 2026, 9:38 p.m. |
Created at: April 29, 2026, 6:01 p.m.