Triple
T10294857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barcelona Sporting Club |
E241456
|
entity |
| Predicate | hasProfessionalFootballSection |
P83129
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Barcelona Sporting Club, hasProfessionalFootballSection, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProfessionalFootballSection Context triple: [Barcelona Sporting Club, hasProfessionalFootballSection, true]
-
A.
hasFootballSubdivision
chosen
Indicates that an organization or institution includes a specific football-related division or sub-unit within its structure.
-
B.
hasProfessionalPlayers
Indicates that an entity is associated with or includes individuals who participate in a profession at a professional level.
-
C.
includesProfessionalLeagues
Indicates that an entity contains or encompasses one or more professional sports leagues within its scope or structure.
-
D.
hasNonLeagueFootballHistory
Indicates that an entity has a history of participation in football competitions outside the official league system.
-
E.
hasProfessionalLeague
Indicates that an entity is associated with or participates in a recognized professional sports league.
- 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_69d381aaafc08190af475ef58dc16aba |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d2d5e0f88190be3e23ba2511a1e9 |
completed | April 7, 2026, 9:48 a.m. |
| PD | Predicate disambiguation | batch_69d4d1f35e548190be3b4d92d65d2d20 |
completed | April 7, 2026, 9:44 a.m. |
Created at: April 6, 2026, 11:42 a.m.