Triple
T680986
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | English football league system |
E13179
|
entity |
| Predicate | professionalTiersCount |
P6578
|
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: [English football league system, professionalTiersCount, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: professionalTiersCount Context triple: [English football league system, professionalTiersCount, 4]
-
A.
teamLevel
Indicates the hierarchical rank or tier at which a team operates within an organization, competition, or structure.
-
B.
numberOfLevels
chosen
Indicates the total count of hierarchical layers, stages, or floors associated with an entity.
-
C.
trainingLevel
Indicates the degree or stage of training or skill development that an entity has attained.
-
D.
designationLevel
Indicates the specific rank, tier, or level assigned to an entity within a designation or classification system.
-
E.
hasGradeCount
Indicates a relationship where an entity is associated with the number of grades it has or has received.
- 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_69a4933d3bf88190972041cd8cf143b9 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a06e294c8190873116a3253e04f9 |
completed | March 1, 2026, 8:24 p.m. |
| PD | Predicate disambiguation | batch_69a49d1d79608190a849ba9ffad2879d |
completed | March 1, 2026, 8:10 p.m. |
Created at: March 1, 2026, 7:36 p.m.