Triple
T8432709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Silves Medieval Fair |
E199152
|
entity |
| Predicate | languageOfPrimaryAudience |
P1252
|
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: [Silves Medieval Fair, languageOfPrimaryAudience, Portuguese]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfPrimaryAudience Context triple: [Silves Medieval Fair, languageOfPrimaryAudience, Portuguese]
-
A.
languageOfPrimaryNarrations
Indicates the language in which the main or primary narrations are expressed or conveyed.
-
B.
primaryLanguageOf
chosen
Indicates that a specified language is the main or official language used by a particular entity (such as a person, organization, or region).
-
C.
languageOfProduct
Indicates the language in which a product is written, labeled, presented, or otherwise made available.
-
D.
dominantMediaLanguage
Indicates that one language is the primary or most prevalent medium of communication used in a given media context or outlet.
-
E.
languageOfPrimaryBroadcasts
Indicates the language in which an entity’s primary broadcasts are delivered.
- 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_69ca8313c99081909a5c6d83b91de5b3 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe30fba4081908bfdef3faf5baceb |
completed | March 31, 2026, 3:06 p.m. |
| PD | Predicate disambiguation | batch_69cbd0ec200c8190b0299e2b0b4bdcc2 |
completed | March 31, 2026, 1:49 p.m. |
Created at: March 30, 2026, 6:07 p.m.