Triple
T8502529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Plantation Preserve Golf Course & Club |
E201254
|
entity |
| Predicate | hasPracticePuttingGreen |
P75785
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Plantation Preserve Golf Course & Club, hasPracticePuttingGreen, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPracticePuttingGreen Context triple: [Plantation Preserve Golf Course & Club, hasPracticePuttingGreen, yes]
-
A.
hasPuttingGreen
chosen
Indicates that an entity includes or provides access to a putting green as a feature or amenity.
-
B.
hasGolfCourse
Indicates that one entity possesses, contains, or includes a golf course as part of its facilities or attributes.
-
C.
hasGrassTypeGreens
Indicates that something possesses or includes green vegetation or grassy plant material.
-
D.
hasGolfDestination
Indicates that one entity serves as a golf-related destination or location for another entity.
-
E.
hasWaterHazards
Indicates that the subject contains or is associated with one or more water-based obstacles or danger areas.
- 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_69ca831fe47c8190b5c57b456d2aefa0 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe59c413881909513d8a9d52333c0 |
completed | March 31, 2026, 3:17 p.m. |
| PD | Predicate disambiguation | batch_69cbd10a4b0881909e254117780dc823 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:14 p.m.