Triple
T5689335
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ponta Negra Beach |
E125389
|
entity |
| Predicate | languageUsedInSurroundingArea |
P29819
|
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: [Ponta Negra Beach, languageUsedInSurroundingArea, Portuguese]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageUsedInSurroundingArea Context triple: [Ponta Negra Beach, languageUsedInSurroundingArea, Portuguese]
-
A.
languageUsedInTourism
Indicates that a particular language is used for communication and services within tourism activities or contexts.
-
B.
isWidelySpokenIn
Indicates that a language is spoken by a large portion of the population across many regions or communities within a specified area.
-
C.
hasLanguageOfSurroundingCountries
Indicates that an entity uses or includes the languages commonly spoken in the countries that geographically surround it.
-
D.
languageUsedAs
Indicates that one language is employed in a specific role, function, or context relative to another entity or situation.
-
E.
languageArea
chosen
Indicates the geographic or cultural region in which a particular language is used or predominantly spoken.
- 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_69c0082bb19c8190823a4facd3cba79b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c029014588819094a2a0f6f9b66bab |
completed | March 22, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69c021c0e0408190ab6c3cd3f907e80f |
completed | March 22, 2026, 5:07 p.m. |
Created at: March 22, 2026, 3:44 p.m.