Triple
T10937539
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leaving Certificate |
E258374
|
entity |
| Predicate | subjectChoice |
P96732
|
FINISHED |
| Object | students typically take 6 to 8 subjects |
—
|
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: students typically take 6 to 8 subjects | Statement: [Leaving Certificate, subjectChoice, students typically take 6 to 8 subjects]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectChoice Context triple: [Leaving Certificate, subjectChoice, students typically take 6 to 8 subjects]
-
A.
academicSelection
Indicates a relationship where an entity chooses or designates another entity for an academic purpose, role, or opportunity.
-
B.
subjectType
Indicates the classification or category that defines what kind of entity the subject is.
-
C.
bestSubject
Indicates that the subject is considered the most outstanding or highest-performing among a set of comparable subjects.
-
D.
subjectInterest
Indicates that the subject has an interest in, or is concerned with, the object.
-
E.
offersFieldOfStudy
Indicates that an institution or program provides a particular field of study as an available area of academic focus.
- 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_69d6aa8769b4819082bfe5e61b9017f0 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d770b065288190b4216beee8e8a193 |
completed | April 9, 2026, 9:26 a.m. |
| PD | Predicate disambiguation | batch_69d72e816a98819096d6c10dfb88a66a |
completed | April 9, 2026, 4:43 a.m. |
| PDg | Predicate description generation | batch_69d7322370648190ba14cdd6fb4cdcb0 |
completed | April 9, 2026, 4:59 a.m. |
Created at: April 8, 2026, 9:23 p.m.