Triple
T35872806
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oberliga (ice hockey) |
E1037273
|
entity |
| Predicate | isProfessionalTier |
P91098
|
FINISHED |
| Object | lower professional tier |
—
|
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: lower professional tier | Statement: [Oberliga (ice hockey), isProfessionalTier, lower professional tier]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isProfessionalTier Context triple: [Oberliga (ice hockey), isProfessionalTier, lower professional tier]
-
A.
semiProfessionalTiers
Indicates a relationship in which entities are organized or classified into tiers that represent semi-professional levels or statuses.
-
B.
hasProfessionalTiers
chosen
Indicates that an entity is organized into or associated with multiple levels or categories of professional status, service, or access.
-
C.
professionalTiers
Indicates a hierarchical relationship that orders professionals into different levels or tiers based on status, role, or qualification.
-
D.
topTierProfessional
Indicates that an entity is recognized as a highly skilled, elite-level professional within its field or domain.
-
E.
professionalTierInCountry
Indicates the professional level or tier that an entity holds within the context of a specific country.
- 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_69f76e1e701c8190a4990d4978ce4fe6 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7aa3883d48190b05e3d2da7a017ae |
completed | May 3, 2026, 8:04 p.m. |
| PD | Predicate disambiguation | batch_69f7a8d435288190b30b1991fb003121 |
completed | May 3, 2026, 7:58 p.m. |
Created at: May 3, 2026, 4:06 p.m.