Triple
T23402838
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Facultad de Odontología |
E559551
|
entity |
| Predicate | áreaDeConocimiento |
P28568
|
FINISHED |
| Object | ciencias de la salud |
—
|
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: ciencias de la salud | Statement: [Facultad de Odontología, áreaDeConocimiento, ciencias de la salud]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: áreaDeConocimiento Context triple: [Facultad de Odontología, áreaDeConocimiento, ciencias de la salud]
-
A.
competenceArea
Indicates that one entity has a particular domain, field, or area in which it possesses competence, expertise, or responsibility.
-
B.
regionOfAcademicFocus
Indicates the academic subject area or discipline that an entity (such as a person or program) primarily concentrates on or specializes in.
-
C.
thematicArea
chosen
Indicates the subject or item is associated with, or falls under, a particular thematic area or topic of focus.
-
D.
materiasQueConocen
Indicates a relationship where certain subjects or topics are known or mastered by specific entities.
-
E.
knowledgeScope
Indicates the extent or range of information, topics, or understanding that an entity possesses or is 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_69e24549610c8190a069d6411ce5f661 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1a4e09ccc81909869d2c5f6d68432 |
completed | April 29, 2026, 6:27 a.m. |
| PD | Predicate disambiguation | batch_69f061ed34288190a2e5e8cae03b0095 |
completed | April 28, 2026, 7:29 a.m. |
Created at: April 17, 2026, 5:37 p.m.