Triple
T37777636
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | America/Sao_Paulo |
E941730
|
entity |
| Predicate | languageCommonlyAssociated |
P20184
|
FINISHED |
| Object | Portuguese |
—
|
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: Portuguese | Statement: [America/Sao_Paulo, languageCommonlyAssociated, Portuguese]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageCommonlyAssociated Context triple: [America/Sao_Paulo, languageCommonlyAssociated, Portuguese]
-
A.
languageCommonlyCalled
Indicates that one language is commonly referred to or known by a particular alternative name or label.
-
B.
linguisticallyRelatedTo
Indicates that two entities are connected through a linguistic relationship, such as sharing a common language, origin, structure, or other language-based association.
-
C.
languageRefersTo
Indicates that a language is used to denote, describe, or refer to a particular entity, concept, or subject.
-
D.
languageAssociation
chosen
Indicates an association or relationship between entities based on a language they use, represent, or are linked to.
-
E.
languageTerm
Indicates that one entity is a linguistic expression (word, phrase, or term) used to denote or label the other entity.
- 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_69f76ee4431881908f87e8892a9f39f3 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a01b31b233c819091974a06f7a76d4a |
completed | May 11, 2026, 10:44 a.m. |
| PD | Predicate disambiguation | batch_6a01b2c147008190bbf9e0fa1bbae1f3 |
completed | May 11, 2026, 10:43 a.m. |
Created at: May 3, 2026, 4:19 p.m.