Triple
T12190997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FC Honka |
E290459
|
entity |
| Predicate | usesSurfaceType |
P58186
|
FINISHED |
| Object | artificial turf |
—
|
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: artificial turf | Statement: [FC Honka, usesSurfaceType, artificial turf]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesSurfaceType Context triple: [FC Honka, usesSurfaceType, artificial turf]
-
A.
usesSurface
chosen
Indicates that one entity employs or interacts with another entity as a surface or platform for its action or function.
-
B.
surfaceType
Indicates the kind or classification of surface associated with an entity or interaction.
-
C.
hasPrimarySurface
Indicates that one entity serves as the main or principal surface associated with another entity.
-
D.
surfaceAccess
Indicates that one entity provides a means for another entity to reach, enter, or interact with a surface or outer layer.
-
E.
surfaceState
Indicates the physical condition or status of a surface, such as its texture, cleanliness, damage, or treatment at a given time.
- 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_69d6ab64de5881908d56eb7a75c6cc69 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d938cd2edc8190b1971349dbc0dee0 |
completed | April 10, 2026, 5:52 p.m. |
| PD | Predicate disambiguation | batch_69d91c38321c819080d500d0d64a04f6 |
completed | April 10, 2026, 3:50 p.m. |
Created at: April 8, 2026, 9:50 p.m.