Triple
T37089414
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ley del Seguro Social |
E918373
|
entity |
| Predicate | tipoDePrestaciones |
P65125
|
FINISHED |
| Object | prestaciones en especie |
—
|
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: prestaciones en especie | Statement: [Ley del Seguro Social, tipoDePrestaciones, prestaciones en especie]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tipoDePrestaciones Context triple: [Ley del Seguro Social, tipoDePrestaciones, prestaciones en especie]
-
A.
tipoDePolíticas
Indicates a classification relationship where specific policies are associated with or assigned to a particular type or category of policies.
-
B.
beneficeType
Indicates the specific category or kind of benefice (ecclesiastical office or endowed church position) associated with an entity.
-
C.
planType
Indicates the specific category or kind of plan associated with an entity, such as its level, structure, or intended use.
-
D.
insuranceType
Indicates the specific category or kind of insurance coverage associated with an entity or relationship.
-
E.
typeOfProvision
chosen
Indicates the specific category or kind of provision that characterizes or qualifies a given arrangement, service, or resource.
- 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_69f76e9952b88190a6fe01ba01476520 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fbad1e94988190b86d447a68e65067 |
completed | May 6, 2026, 9:05 p.m. |
| PD | Predicate disambiguation | batch_69fba881b8e0819094790935152b99a1 |
completed | May 6, 2026, 8:45 p.m. |
Created at: May 3, 2026, 4:14 p.m.