Triple
T24179265
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | School of Tourism and Hotel Management |
E599369
|
entity |
| Predicate | commonCoursesInclude |
P11940
|
FINISHED |
| Object | introduction to tourism |
—
|
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: introduction to tourism | Statement: [School of Tourism and Hotel Management, commonCoursesInclude, introduction to tourism]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commonCoursesInclude Context triple: [School of Tourism and Hotel Management, commonCoursesInclude, introduction to tourism]
-
A.
commonCourse
Indicates that two or more entities share at least one course in common.
-
B.
typicalCourse
chosen
Indicates that one entity is a standard or commonly taken course associated with another entity, such as a program, curriculum, or field of study.
-
C.
courseIncludes
Indicates that a course contains or covers a particular component, such as a topic, module, lesson, or resource.
-
D.
notableCourseType
Indicates that a course has a particular notable or distinguished type or classification (e.g., flagship, honors, or otherwise specially recognized).
-
E.
hasTypicalCourse
Indicates that there is a characteristic or commonly observed progression, sequence, or development pattern associated with the subject.
- 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_69e288cca05481908faeb1563711114a |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f27c9ddfcc819096697a844b300cce |
completed | April 29, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69f1c42f942c8190b103ff29a60fef34 |
completed | April 29, 2026, 8:41 a.m. |
Created at: April 17, 2026, 11:34 p.m.