Triple
T13817490
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cortes Superiores de Justicia |
E332054
|
entity |
| Predicate | sedeTípica |
P48956
|
FINISHED |
| Object | capital del distrito judicial respectivo |
—
|
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: capital del distrito judicial respectivo | Statement: [Cortes Superiores de Justicia, sedeTípica, capital del distrito judicial respectivo]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sedeTípica Context triple: [Cortes Superiores de Justicia, sedeTípica, capital del distrito judicial respectivo]
-
A.
typicalSeat
chosen
Indicates the usual or standard seating position or location associated with an entity in a given context.
-
B.
typicalIn
Indicates that something commonly occurs, appears, or is found within a given context, category, or environment.
-
C.
typicalSettlement
Indicates that the subject is a common or characteristic type of settlement typically found in the context of the object.
-
D.
typicalBase
Indicates that one entity serves as the standard or most representative base or foundation for another entity in typical or common cases.
-
E.
typicalHouse
Indicates that something is a standard or representative example of a house in terms of its usual features, structure, or characteristics.
- 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_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. |
Created at: April 9, 2026, 10:12 p.m.