Triple
T13328067
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Turkish football league system |
E317492
|
entity |
| Predicate | professionalLevels |
P91106
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Turkish football league system, professionalLevels, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: professionalLevels Context triple: [Turkish football league system, professionalLevels, 4]
-
A.
professionalTiers
chosen
Indicates a hierarchical relationship that orders professionals into different levels or tiers based on status, role, or qualification.
-
B.
professionalClass
Indicates that an entity belongs to, or is categorized within, a particular professional or occupational class.
-
C.
professionalTiersOrganisedBy
Indicates that professional tiers or levels are structured, arranged, or classified according to the organizing criterion or entity specified.
-
D.
semiProfessionalTiers
Indicates a relationship in which entities are organized or classified into tiers that represent semi-professional levels or statuses.
-
E.
professionalScope
Indicates the range of activities, responsibilities, or roles that fall within a person’s or organization’s recognized professional duties or expertise.
- 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_69d806b4d62c81908d4ced1665414be5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99cfdc9388190af1fdd3cd4717bd8 |
completed | April 11, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69d98f6babd88190a5d529df9584b9a4 |
completed | April 11, 2026, 12:01 a.m. |
Created at: April 9, 2026, 9:30 p.m.