Triple
T15962812
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tunisian football league system |
E387103
|
entity |
| Predicate | semiProfessionalLevels |
P17465
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [Tunisian football league system, semiProfessionalLevels, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: semiProfessionalLevels Context triple: [Tunisian football league system, semiProfessionalLevels, 3]
-
A.
semiProfessionalTiers
chosen
Indicates a relationship in which entities are organized or classified into tiers that represent semi-professional levels or statuses.
-
B.
professionalTiers
Indicates a hierarchical relationship that orders professionals into different levels or tiers based on status, role, or qualification.
-
C.
professionalClass
Indicates that an entity belongs to, or is categorized within, a particular professional or occupational class.
-
D.
professionalTierInCountry
Indicates the professional level or tier that an entity holds within the context of a specific country.
-
E.
professionalTiersOrganisedBy
Indicates that professional tiers or levels are structured, arranged, or classified according to the organizing criterion or entity specified.
- 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_69d86da882448190a82ea962fe343b79 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e173b3bf6c81909230170e833d7ce7 |
completed | April 16, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69e142d6fb588190b4176eab4bbae774 |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:53 a.m.