Triple
T38078503
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Selena Etc. boutiques |
E950785
|
entity |
| Predicate | hasSecondaryLanguageOfBusiness |
P194011
|
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: [Selena Etc. boutiques, hasSecondaryLanguageOfBusiness, Spanish]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSecondaryLanguageOfBusiness Context triple: [Selena Etc. boutiques, hasSecondaryLanguageOfBusiness, Spanish]
-
A.
hasSecondaryLanguage
Indicates that an entity possesses or uses a secondary language in addition to its primary language.
-
B.
hasSecondaryLanguageFamily
Indicates that an entity has an additional, non-primary association with a particular language family.
-
C.
hasSecondaryNationalLanguage
Indicates that an entity possesses an officially recognized secondary national language in addition to its primary national language.
-
D.
secondaryLanguageSupport
chosen
Indicates that an entity provides assistance, services, or functionality in an additional (non-primary) language.
-
E.
hasSecondaryLanguageNearby
Indicates that an entity has at least one secondary language present or used in its immediate vicinity or surrounding 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_69f76f02a6c48190a94f3c0b3ee90cf2 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a00b8e0a5508190abc5c1e492bed12e |
completed | May 10, 2026, 4:57 p.m. |
| PD | Predicate disambiguation | batch_6a00b8327d048190850af317f60f0f8b |
completed | May 10, 2026, 4:54 p.m. |
Created at: May 3, 2026, 4:21 p.m.