Triple
T38640586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Real Madrid CF |
E938581
|
entity |
| Predicate | hasFormerReserveTeamName |
P92907
|
FINISHED |
| Object | Real Madrid Aficionados |
—
|
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: Real Madrid Aficionados | Statement: [Real Madrid CF, hasFormerReserveTeamName, Real Madrid Aficionados]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFormerReserveTeamName Context triple: [Real Madrid CF, hasFormerReserveTeamName, Real Madrid Aficionados]
-
A.
hasFormerTeam
Indicates that an entity was previously a member of, played for, or was affiliated with a team in the past but is no longer associated with that team.
-
B.
hasFormerClub
Indicates that an entity (typically a person, such as an athlete) previously belonged to or was a member of a particular club or organization.
-
C.
clubFormerName
chosen
Indicates that a sports club previously operated under a different official name.
-
D.
predecessorClubNickname
Indicates that one club’s nickname was previously used by another club that served as its predecessor.
-
E.
associatedWithFormerClubName
Indicates a relationship where an entity is linked to the previous or former name of a club.
- 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_69f76ed948ec81908ce7811608a8f359 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fef8c3f2388190b995ec173512945a |
completed | May 9, 2026, 9:05 a.m. |
| PD | Predicate disambiguation | batch_69fef65975608190960b78d27e806d4f |
completed | May 9, 2026, 8:54 a.m. |
Created at: May 3, 2026, 4:32 p.m.