Triple
T13817474
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cortes Superiores de Justicia |
E332054
|
entity |
| Predicate | materiasQueConocen |
P111615
|
FINISHED |
| Object | materia civil |
—
|
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: materia civil | Statement: [Cortes Superiores de Justicia, materiasQueConocen, materia civil]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: materiasQueConocen Context triple: [Cortes Superiores de Justicia, materiasQueConocen, materia civil]
-
A.
areTaughtIn
Indicates that certain subjects, courses, or topics are instructed or delivered within specific locations, classes, or educational settings.
-
B.
languageOfSubjects
Indicates the language used by or associated with the subjects in question.
-
C.
teachingSubject
Indicates that an entity is engaged in teaching or instructing another entity in a particular subject or field of knowledge.
-
D.
widelyStudiedIn
Indicates that something has been extensively researched, analyzed, or examined within a particular field, domain, or context.
-
E.
commonlyTaughtWith
Indicates that two subjects or concepts are frequently taught together within the same course, lesson, or curriculum 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_69d81c59f8808190a851bc56afdc55e9 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de0281bb988190803ee195f430b9c8 |
completed | April 14, 2026, 9:01 a.m. |
| PD | Predicate disambiguation | batch_69dbc862e9608190bd8a3d883959b7e4 |
completed | April 12, 2026, 4:29 p.m. |
| PDg | Predicate description generation | batch_69dcad0eea9881908f71e1eed9a2446b |
completed | April 13, 2026, 8:45 a.m. |
Created at: April 9, 2026, 10:12 p.m.