Triple
T38273633
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | El Cobre |
E1021282
|
entity |
| Predicate | distanceFromSantiagoDeCuba |
P18761
|
FINISHED |
| Object | approximately 18 kilometers |
—
|
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: approximately 18 kilometers | Statement: [El Cobre, distanceFromSantiagoDeCuba, approximately 18 kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromSantiagoDeCuba Context triple: [El Cobre, distanceFromSantiagoDeCuba, approximately 18 kilometers]
-
A.
distanceFromSantoDomingo
Indicates the spatial distance between a given entity and the location of Santo Domingo.
-
B.
distanceToSantiago_km
Indicates the physical distance, measured in kilometers, between a given location and Santiago.
-
C.
distanceFromSantiago
chosen
Indicates the spatial distance between a given entity and the location of Santiago.
-
D.
distanceFromHolguínCity
Indicates the spatial distance separating an entity from Holguín City.
-
E.
distanceToHolguin
Indicates the measured spatial distance between a given entity and the location of Holguin.
- 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_69f76dee198c8190bf5109421e47a658 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a037c903be48190a2fafa53d7d50d42 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a1c850c819088795a7ae59bdeb8 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:30 p.m.