Triple
T6123124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | French Lick Resort Casino |
E136529
|
entity |
| Predicate | hasNumberOfGolfCourses |
P21556
|
FINISHED |
| Object | three |
—
|
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: three | Statement: [French Lick Resort Casino, hasNumberOfGolfCourses, three]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfGolfCourses Context triple: [French Lick Resort Casino, hasNumberOfGolfCourses, three]
-
A.
numberOfGolfCourses
chosen
Indicates the total count of golf courses associated with a given entity.
-
B.
hasGolfCourse
Indicates that one entity possesses, contains, or includes a golf course as part of its facilities or attributes.
-
C.
hasGolfCourseCluster
Indicates that an entity is associated with or contains a group or concentration of golf courses in a particular area.
-
D.
hasGolfDestination
Indicates that one entity serves as a golf-related destination or location for another entity.
-
E.
hasCountryClubLocation
Indicates that a country club is located at, or associated with, a specific geographic place or address.
- 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_69c0089f851c81909e5e189a617dcff6 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c05c247b7081909972b40afb165e6f |
completed | March 22, 2026, 9:16 p.m. |
| PD | Predicate disambiguation | batch_69c049f9ab3c81909c8ab6466f6a2935 |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:14 p.m.