Triple
T30201752
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Virginia, Montserrado County |
E767798
|
entity |
| Predicate | distanceToMonrovia |
P202949
|
FINISHED |
| Object | approximately 15 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 15 kilometers | Statement: [Virginia, Montserrado County, distanceToMonrovia, approximately 15 kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToMonrovia Context triple: [Virginia, Montserrado County, distanceToMonrovia, approximately 15 kilometers]
-
A.
distanceFromMonrovia
chosen
Indicates the measured spatial distance between an entity’s location and the city of Monrovia.
-
B.
distanceFromConakry
Indicates the spatial distance between a given entity and the location of Conakry.
-
C.
distanceFromBanjul
Indicates the spatial distance between a given location and the city of Banjul.
-
D.
distanceFromCotonou
Indicates the measured spatial distance between a given location and the city of Cotonou.
-
E.
distanceToPort-au-Prince
Indicates the spatial distance between a given location and the city of Port-au-Prince.
- 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_69f2247db1108190835c0727c97637c3 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_6a0116ca6a1c81909a9e938dbe54eb8a |
completed | May 10, 2026, 11:37 p.m. |
| PD | Predicate disambiguation | batch_6a0116655e248190bdfe5b4bb4f1cf47 |
completed | May 10, 2026, 11:36 p.m. |
Created at: April 29, 2026, 7:30 p.m.