Triple
T10591244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Morongo Golf Club at Tukwet Canyon |
E249991
|
entity |
| Predicate | courseCountDescription |
P49837
|
FINISHED |
| Object | two 18-hole courses |
—
|
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: two 18-hole courses | Statement: [Morongo Golf Club at Tukwet Canyon, courseCountDescription, two 18-hole courses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: courseCountDescription Context triple: [Morongo Golf Club at Tukwet Canyon, courseCountDescription, two 18-hole courses]
-
A.
numberOfCourses
chosen
Indicates the quantity of courses associated with a given entity.
-
B.
courseCountVariability
Indicates how much the number of courses taken or offered varies across different times, groups, or conditions.
-
C.
courseType
Indicates the classification or category of a course based on its nature, level, or instructional format.
-
D.
coursePar
Indicates that two entities (such as paths, lines, or trajectories) run alongside each other in the same general direction without intersecting.
-
E.
hasNumberOfLessons
Indicates the specific count of lessons associated with an entity.
- 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_69d381c9d3d48190a29ee491e1696a0e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d5277b66448190b668c47fe6af4f3d |
completed | April 7, 2026, 3:49 p.m. |
| PD | Predicate disambiguation | batch_69d51907b2b881908ab9a8594688ee06 |
completed | April 7, 2026, 2:47 p.m. |
Created at: April 6, 2026, 12:40 p.m.