Triple
T1258040
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Glenrothes Golf Course |
E12436
|
entity |
| Predicate | hasGreenType |
P24953
|
FINISHED |
| Object | grass greens |
—
|
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: grass greens | Statement: [Glenrothes Golf Course, hasGreenType, grass greens]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGreenType Context triple: [Glenrothes Golf Course, hasGreenType, grass greens]
-
A.
haveType
Indicates that an entity belongs to or is classified under a specified type or category.
-
B.
hasTypeGenus
Indicates that one entity is the type genus that formally defines or represents the taxonomic group of the other entity.
-
C.
hasPetrovType
Indicates that an entity (typically a spacetime or gravitational field) is classified as having a specific Petrov type in the Petrov classification of its Weyl tensor.
-
D.
hasFruitType
Indicates that an entity possesses or is associated with a specific type or category of fruit.
-
E.
hasTypeName
Indicates that an entity is associated with a specific type name used to classify or identify its kind.
- 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_69a4933352e08190ac617291985e76c0 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4bfaa2b508190a3f61c67b3fa3ad4 |
completed | March 1, 2026, 10:37 p.m. |
| PD | Predicate disambiguation | batch_69a4bb6c977c8190a2bf3e8b67a59beb |
completed | March 1, 2026, 10:19 p.m. |
| PDg | Predicate description generation | batch_69a4bc4a1f048190bd1ddcc4cc3fe057 |
completed | March 1, 2026, 10:23 p.m. |
Created at: March 1, 2026, 7:50 p.m.