Triple
T37089410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ley del Seguro Social |
E918373
|
entity |
| Predicate | lengua |
P123203
|
FINISHED |
| Object | español |
—
|
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: español | Statement: [Ley del Seguro Social, lengua, español]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lengua Context triple: [Ley del Seguro Social, lengua, español]
-
A.
мова
chosen
Indicates that an entity uses, is expressed in, or is associated with a particular language.
-
B.
vernacularOf
Indicates that one language or dialect is the everyday, locally used form corresponding to another, more general or standard language.
-
C.
languageAttestedIn
Indicates that evidence exists showing the use or presence of a particular language in a specified place, time, or context.
-
D.
macrolanguageOf
Indicates that one language functions as a macrolanguage encompassing or grouping together one or more related individual languages.
-
E.
isLanguageOf
Indicates that a particular language is used as the official or primary language associated with a given entity (such as a person, document, or region).
- 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_69fb344c60f8819090f2e21e1e61d621 |
completed | May 6, 2026, 12:30 p.m. |
| PD | Predicate disambiguation | batch_69fb2f642db08190b562725502c74ea6 |
completed | May 6, 2026, 12:09 p.m. |
Created at: May 3, 2026, 4:14 p.m.