Triple
T1811280
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sambadrome Marquês de Sapucaí |
E40336
|
entity |
| Predicate | hasPhotoOpportunity |
P23855
|
FINISHED |
| Object | Carnival parades |
—
|
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: Carnival parades | Statement: [Sambadrome Marquês de Sapucaí, hasPhotoOpportunity, Carnival parades]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPhotoOpportunity Context triple: [Sambadrome Marquês de Sapucaí, hasPhotoOpportunity, Carnival parades]
-
A.
hasPhotograph
Indicates that one entity possesses, includes, or is associated with a photograph depicting or representing another entity.
-
B.
hasCamera
Indicates that an entity is equipped with or possesses a camera.
-
C.
hasAIPhotoFeatures
Indicates that an entity provides or supports photo-related features powered by artificial intelligence.
-
D.
meetsInCamera
Indicates that two or more entities are physically present together in the same camera frame or shot at the same time.
-
E.
hasPhotographicSignificance
chosen
Indicates that something holds notable importance or relevance in the context of photography, such as for documentation, artistic value, or visual record.
- 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_69a88643a3388190a612f2ebe1fb29e7 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab694d75ac8190a4d61399c04b9fb9 |
completed | March 6, 2026, 11:54 p.m. |
| PD | Predicate disambiguation | batch_69aa61d6b8ec8190a1597b2e44ea6534 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:32 p.m.