Triple
T11517637
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Isla de Pinos |
E273071
|
entity |
| Predicate | distanceFromMainlandCuba |
P5691
|
FINISHED |
| Object | about 50 to 60 kilometers south of mainland Cuba |
—
|
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 50 to 60 kilometers south of mainland Cuba | Statement: [Isla de Pinos, distanceFromMainlandCuba, about 50 to 60 kilometers south of mainland Cuba]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromMainlandCuba Context triple: [Isla de Pinos, distanceFromMainlandCuba, about 50 to 60 kilometers south of mainland Cuba]
-
A.
distanceToHavana
Indicates the measured spatial distance between a given entity’s location and the city of Havana.
-
B.
distanceFromMainland
chosen
Indicates the measured spatial separation between a location and the nearest point on the mainland.
-
C.
separatedFromCubaBy
Indicates that one entity is divided or kept apart from Cuba by a specified physical or conceptual boundary.
-
D.
distanceToHaiti
Indicates the measured or calculated spatial distance between a given entity or location and the country of Haiti.
-
E.
distanceFromPortOfSpain
Indicates the measured distance between a given location and the city of Port of Spain.
- 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_69d6aae2c3748190bed2ea50dfb160dc |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d87fcc72a48190b81acedfcc8685d3 |
completed | April 10, 2026, 4:42 a.m. |
| PD | Predicate disambiguation | batch_69d80876e5f0819088cff2e72f773cf6 |
completed | April 9, 2026, 8:13 p.m. |
Created at: April 8, 2026, 9:36 p.m.