Triple
T14142412
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Casemiro |
E350458
|
entity |
| Predicate | shirtNumberAtRealMadrid |
P2651
|
FINISHED |
| Object | 14 |
—
|
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: 14 | Statement: [Casemiro, shirtNumberAtRealMadrid, 14]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: shirtNumberAtRealMadrid Context triple: [Casemiro, shirtNumberAtRealMadrid, 14]
-
A.
clubNumberAtManchesterUnited
Indicates the squad number a player wears or wore while playing for Manchester United.
-
B.
shirtNumberTradition
Indicates a conventional or historically established assignment or significance of a particular shirt number within a team or sport.
-
C.
leftRealMadrid
Indicates that an entity departed from or is no longer a member of Real Madrid (e.g., a player leaving the club).
-
D.
shirtNumberType
Indicates the type or category of a shirt number assigned to an entity (for example, a player’s jersey number type).
-
E.
jerseyNumber
chosen
Indicates the specific uniform number assigned to and worn by an individual, typically in a sports context.
- 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_69d827865f608190b311820428ae027b |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de611ed3508190add37baa30d1d134 |
completed | April 14, 2026, 3:45 p.m. |
| PD | Predicate disambiguation | batch_69de05b5e7a08190a16be9ad8b92b80c |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 10, 2026, 12:48 a.m.