Triple
T921716
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trump National Doral Miami |
E19898
|
entity |
| Predicate | numberOfGolfCourses |
P21556
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Trump National Doral Miami, numberOfGolfCourses, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfGolfCourses Context triple: [Trump National Doral Miami, numberOfGolfCourses, 4]
-
A.
hasGolfCourse
Indicates that one entity possesses, contains, or includes a golf course as part of its facilities or attributes.
-
B.
numberOfVenues
Indicates the total count of venues associated with a given entity or context.
-
C.
numberOfCourts
Indicates the quantity of courts associated with or present at a given entity or location.
-
D.
numberOfSites
Indicates the total count of distinct sites associated with or involved in the given entity or context.
-
E.
numberOfSports
Indicates the quantity of distinct sports associated with or involved in a given 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_69a493a099788190a696d9d8408cbaf4 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b313cb908190ad78b3a54e4f2eb7 |
completed | March 1, 2026, 9:43 p.m. |
| PD | Predicate disambiguation | batch_69a4b295b02481908e5f53bfcb83cc94 |
completed | March 1, 2026, 9:41 p.m. |
| PDg | Predicate description generation | batch_69a4b30efd2c8190b780a6dee086d0aa |
completed | March 1, 2026, 9:43 p.m. |
Created at: March 1, 2026, 7:40 p.m.