Triple
T4417820
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bayonne Golf Club |
E95019
|
entity |
| Predicate | coursePar |
P55540
|
FINISHED |
| Object | 71 |
—
|
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: 71 | Statement: [Bayonne Golf Club, coursePar, 71]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coursePar Context triple: [Bayonne Golf Club, coursePar, 71]
-
A.
course
Indicates that an entity is an academic class or unit of instruction offered within an educational program.
-
B.
courseName
Indicates the specific name or title assigned to a course in an educational context.
-
C.
courseStructure
Indicates how a course is organized into its constituent parts, such as modules, units, lessons, and their sequencing or hierarchy.
-
D.
courseType
Indicates the classification or category of a course based on its nature, level, or instructional format.
-
E.
typicalCourse
Indicates that one entity is a standard or commonly taken course associated with another entity, such as a program, curriculum, or field of study.
- 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_69b3453a36908190b95a79a297ca083c |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3551d5d7481908528c2de0a6fda06 |
completed | March 13, 2026, 12:06 a.m. |
| PD | Predicate disambiguation | batch_69b34f5d0c54819085c08533bb58030a |
completed | March 12, 2026, 11:42 p.m. |
| PDg | Predicate description generation | batch_69b34ff7018c81908ad8597e525c042b |
completed | March 12, 2026, 11:44 p.m. |
Created at: March 12, 2026, 11:29 p.m.