Triple
T3924802
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Biblioteca Central de la Universidad Nacional Mayor de San Marcos |
E93249
|
entity |
| Predicate | languageOfCatalog |
P876
|
FINISHED |
| Object | Spanish |
—
|
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: Spanish | Statement: [Biblioteca Central de la Universidad Nacional Mayor de San Marcos, languageOfCatalog, Spanish]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfCatalog Context triple: [Biblioteca Central de la Universidad Nacional Mayor de San Marcos, languageOfCatalog, Spanish]
-
A.
languageOfProduct
Indicates the language in which a product is written, labeled, presented, or otherwise made available.
-
B.
languageOfRecords
chosen
Indicates the language in which the records are written or maintained.
-
C.
presentedInLanguage
Indicates that something (such as content, information, or a work) is expressed or made available using a particular language.
-
D.
languageCodeISO639-1
Indicates that the subject entity is associated with the specified two-letter ISO 639-1 language code.
-
E.
contentLanguage
Indicates the language in which the content is expressed or intended to be understood.
- 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_69aed96bfa1081908f7b30f2c647dee6 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aeed7dacdc8190854ebc13db2d24bc |
completed | March 9, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69aee7609c4081908000ce12ae827c3f |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:23 p.m.