Triple
T2402960
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Collège de France |
E50209
|
entity |
| Predicate | tuitionFee |
P39199
|
FINISHED |
| Object | none |
—
|
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: none | Statement: [Collège de France, tuitionFee, none]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tuitionFee Context triple: [Collège de France, tuitionFee, none]
-
A.
tuitionFeeType
Indicates the category or structure of tuition fees that applies to an entity (such as full-time, part-time, in-state, out-of-state, or other fee types).
-
B.
tuitionPolicy
Indicates the rules or guidelines governing how tuition is determined, charged, or managed for an educational program or institution.
-
C.
admissionFee
Indicates the monetary charge required for entry or participation in a place, event, or activity.
-
D.
educationType
Indicates the specific category or level of education associated with an entity, such as formal, informal, primary, secondary, or higher education.
-
E.
studiedUnder
Indicates that one entity received instruction, training, or mentorship from another, typically in an academic or apprenticeship context.
- 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_69a88b0339a88190a1207333cd271cc9 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abceab9ce881909ae0a2f34515c11e |
completed | March 7, 2026, 7:07 a.m. |
| PD | Predicate disambiguation | batch_69abc5a530e8819094105aa92dfaf6b3 |
completed | March 7, 2026, 6:28 a.m. |
| PDg | Predicate description generation | batch_69abceaa42b88190a790355100fede3d |
completed | March 7, 2026, 7:07 a.m. |
Created at: March 4, 2026, 7:58 p.m.