Triple
T35814707
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UdL |
E1035325
|
entity |
| Predicate | focusesOnAcademicArea |
P778
|
FINISHED |
| Object | agrifood and forestry sciences |
—
|
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: agrifood and forestry sciences | Statement: [UdL, focusesOnAcademicArea, agrifood and forestry sciences]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: focusesOnAcademicArea Context triple: [UdL, focusesOnAcademicArea, agrifood and forestry sciences]
-
A.
regionOfAcademicFocus
Indicates the academic subject area or discipline that an entity (such as a person or program) primarily concentrates on or specializes in.
-
B.
regionOfAcademicInterest
Indicates that an entity has a particular academic field or subject area as its focus of interest or study.
-
C.
academicFocus
chosen
Indicates the primary field of study, discipline, or subject area that an entity concentrates on academically.
-
D.
regionOfStudy
Indicates the academic or research area that is the focus of someone’s study or investigation.
-
E.
disciplinaryFocus
Indicates the primary academic or professional field, subject area, or discipline that something is centered on or concerned with.
- 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_69f76e1762408190b885a8456862e372 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fd35d108908190b79b1e8e6bbd62aa |
completed | May 8, 2026, 1:01 a.m. |
| PD | Predicate disambiguation | batch_69fd34cb46108190b43c3b7f67ec4cd4 |
completed | May 8, 2026, 12:56 a.m. |
Created at: May 3, 2026, 4:06 p.m.