Triple
T27850860
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Golf Club St. Leon-Rot |
E703947
|
entity |
| Predicate | hasPracticeGreens |
P196861
|
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: [Golf Club St. Leon-Rot, hasPracticeGreens, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPracticeGreens Context triple: [Golf Club St. Leon-Rot, hasPracticeGreens, true]
-
A.
hasGrassTypeGreens
Indicates that something possesses or includes green vegetation or grassy plant material.
-
B.
hasGreenBeltStatus
Indicates that an entity holds a recognized "green belt" level or certification within a defined ranking or qualification system.
-
C.
hasGreenRuns
Indicates that an entity possesses or includes ski runs that are classified as green (i.e., beginner-level).
-
D.
hasGreenBeltContext
Indicates that an entity is associated with or situated within a green belt area or planning context.
-
E.
hasVillageGreen
Indicates that one entity possesses or includes a village green as part of its area or facilities.
- 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_69ef840e614c8190a88cf9638c14a265 |
completed | April 27, 2026, 3:43 p.m. |
| NER | Named-entity recognition | batch_69fe6c811bcc81908b1e1b1f8bcb071b |
completed | May 8, 2026, 11:06 p.m. |
| PD | Predicate disambiguation | batch_69fe6c026d5481908b7a814dcf38c183 |
completed | May 8, 2026, 11:04 p.m. |
| PDg | Predicate description generation | batch_69fe6c7fc4388190aa88993d00872d7f |
completed | May 8, 2026, 11:06 p.m. |
Created at: April 27, 2026, 6:10 p.m.