Triple
T5568884
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fernando de Noronha Archipelago |
E145946
|
entity |
| Predicate | distanceFromBrazilianCoast |
P5691
|
FINISHED |
| Object | about 354 kilometers east of mainland Brazil |
—
|
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: about 354 kilometers east of mainland Brazil | Statement: [Fernando de Noronha Archipelago, distanceFromBrazilianCoast, about 354 kilometers east of mainland Brazil]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromBrazilianCoast Context triple: [Fernando de Noronha Archipelago, distanceFromBrazilianCoast, about 354 kilometers east of mainland Brazil]
-
A.
distanceFromCoast
Indicates the measured spatial separation between a location and the nearest point on a coastline.
-
B.
distanceToSouthAmerica
Indicates the spatial distance between a given entity’s location and the continent of South America.
-
C.
distanceToPacificOcean
Indicates the physical distance between a given location or entity and the Pacific Ocean.
-
D.
distanceFromMainland
chosen
Indicates the measured spatial separation between a location and the nearest point on the mainland.
-
E.
distanceToSãoPaulo
Indicates the spatial distance between a given entity’s location and the city of São Paulo.
- 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_69c008fdae24819081aa002ad99cd966 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c0203789688190a0bab723186214ad |
completed | March 22, 2026, 5 p.m. |
| PD | Predicate disambiguation | batch_69c01b12826c8190969a584d0f53aa44 |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:36 p.m.