Triple
T21209384
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Course at Torrey Pines |
E522678
|
entity |
| Predicate | cityCourse |
P143560
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [South Course at Torrey Pines, cityCourse, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cityCourse Context triple: [South Course at Torrey Pines, cityCourse, true]
-
A.
courseCup
Indicates that a particular cup is used for or associated with serving a specific course in a meal.
-
B.
trainingCity
Indicates the city where an entity receives or conducts training.
-
C.
coursePar
Indicates that two entities (such as paths, lines, or trajectories) run alongside each other in the same general direction without intersecting.
-
D.
taughtInCity
Indicates that a teaching activity or educational instruction took place within a specific city.
-
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_69e0b5112d8881909510b2dcdc93106d |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e73436eb4c819082e05caf1ba52672 |
completed | April 21, 2026, 8:24 a.m. |
| PD | Predicate disambiguation | batch_69e5f6094e3c81909ee9699e00d371f7 |
completed | April 20, 2026, 9:46 a.m. |
| PDg | Predicate description generation | batch_69e5fa92a2448190896c022dd27511ad |
completed | April 20, 2026, 10:06 a.m. |
Created at: April 16, 2026, 3:26 p.m.