Triple
T32600906
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Belgian National Division 1 |
E833366
|
entity |
| Predicate | isSemiProfessional |
P174636
|
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: [Belgian National Division 1, isSemiProfessional, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isSemiProfessional Context triple: [Belgian National Division 1, isSemiProfessional, true]
-
A.
wasAmateurOrSemiPro
Indicates that the subject participated in an activity at an amateur or semi-professional level, rather than as a full professional.
-
B.
isAmateurOrProfessional
Indicates that an entity participates in an activity either at an amateur level or a professional level.
-
C.
isAmateur
Indicates that an entity engages in an activity or field on a non-professional, typically unpaid or hobbyist basis.
-
D.
semiProfessionalTiers
Indicates a relationship in which entities are organized or classified into tiers that represent semi-professional levels or statuses.
-
E.
isProfessionalCompetition
Indicates that the relationship involves a competition conducted at a professional level, typically among participants who engage in the activity as their occupation or primary paid pursuit.
- F. None of above. chosen
Provenance (4 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_69f3492ab63c8190aec24d5003b47c29 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6c69918108190afce924b83211903 |
completed | May 3, 2026, 3:52 a.m. |
| PD | Predicate disambiguation | batch_69f6bd2c138481908afa3ee3e91f8900 |
completed | May 3, 2026, 3:12 a.m. |
| PDg | Predicate description generation | batch_69f6c2df27ec8190912ec8eb488836d0 |
completed | May 3, 2026, 3:37 a.m. |
Created at: May 1, 2026, 1:05 a.m.