Triple
T13745843
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Big Brother Brasil |
E330207
|
entity |
| Predicate | cameraCoverage |
P85626
|
FINISHED |
| Object | multiple fixed and mobile cameras |
—
|
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: multiple fixed and mobile cameras | Statement: [Big Brother Brasil, cameraCoverage, multiple fixed and mobile cameras]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cameraCoverage Context triple: [Big Brother Brasil, cameraCoverage, multiple fixed and mobile cameras]
-
A.
sensorCoverage
chosen
Indicates that a sensor’s detection or monitoring area spatially covers or includes a given region, object, or point.
-
B.
meetsInCamera
Indicates that two or more entities are physically present together in the same camera frame or shot at the same time.
-
C.
stageCoverage
Indicates that one entity provides or has coverage, representation, or support for another within a particular stage or phase of a process or workflow.
-
D.
azimuthCoverage
Indicates the range or extent of directional angles (azimuths) over which something, such as a sensor or system, provides coverage or operates.
-
E.
cameraConfiguration
Indicates the specific setup or arrangement of a camera’s parameters or components in a given 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_69d81c573f288190aa2403d484fa3d49 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de0211ba5481909fbd5b447e3d5a02 |
completed | April 14, 2026, 9 a.m. |
| PD | Predicate disambiguation | batch_69dbbe950b148190ba0df8a749269ec6 |
completed | April 12, 2026, 3:47 p.m. |
Created at: April 9, 2026, 10:08 p.m.