Triple
T5485258
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Torrey Pines Golf Course |
E123563
|
entity |
| Predicate | hasGrassTypeGreens |
P64312
|
FINISHED |
| Object | Poa annua |
—
|
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: Poa annua | Statement: [Torrey Pines Golf Course, hasGrassTypeGreens, Poa annua]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGrassTypeGreens Context triple: [Torrey Pines Golf Course, hasGrassTypeGreens, Poa annua]
-
A.
fairwayGrassType
Indicates the type or variety of grass used on a golf course fairway.
-
B.
isGreenSpaceType
Indicates that one entity is classified as a type or category of green space (such as parks, gardens, or natural vegetated areas) in relation to another.
-
C.
hasGreenType
Indicates that an entity possesses or is associated with a type classified as green.
-
D.
fairwayType
Indicates the specific kind or classification of a fairway associated with an entity.
-
E.
isGreenSpaceFor
Indicates that one entity serves as a designated green or open space intended for use or benefit by another entity.
- 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_69bd4648883481909e9775d43300c5fa |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd93e5d0f08190a6cc9fc408b7c5bb |
completed | March 20, 2026, 6:37 p.m. |
| PD | Predicate disambiguation | batch_69bd91a73b148190a865243536a4fe76 |
completed | March 20, 2026, 6:27 p.m. |
| PDg | Predicate description generation | batch_69bd93e4d2d081908eb75ee22fe72824 |
completed | March 20, 2026, 6:37 p.m. |
Created at: March 20, 2026, 2:10 p.m.