Triple
T30654680
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ley del Matrimonio Civil |
E780352
|
entity |
| Predicate | efecto |
P53074
|
FINISHED |
| Object | traslada la competencia sobre el matrimonio a autoridades civiles |
—
|
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: traslada la competencia sobre el matrimonio a autoridades civiles | Statement: [Ley del Matrimonio Civil, efecto, traslada la competencia sobre el matrimonio a autoridades civiles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: efecto Context triple: [Ley del Matrimonio Civil, efecto, traslada la competencia sobre el matrimonio a autoridades civiles]
-
A.
اثر
Indicates a causal or influential relationship where one entity produces, changes, or leaves an impact on another.
-
B.
اثر
Indicates that one entity has an effect, influence, or impact on another entity.
-
C.
eventEffect
chosen
Indicates the resulting change, outcome, or consequence that one event has on another state, entity, or event.
-
D.
visualEffect
Indicates that one entity produces, modifies, or is associated with a particular visual effect on another entity or within a scene.
-
E.
showsEffect
Indicates that one entity produces, demonstrates, or reveals a particular effect or outcome on another entity or context.
- 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_69f224a5d2b481908a6853cd0138e2d7 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f68a9b821c8190bf6d788b02a58baa |
completed | May 2, 2026, 11:36 p.m. |
| PD | Predicate disambiguation | batch_69f6861170d08190bb98be609d436f84 |
completed | May 2, 2026, 11:17 p.m. |
Created at: April 29, 2026, 8:30 p.m.