Triple
T19196289
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elbasan |
E469975
|
entity |
| Predicate | approximateDistanceToTirana |
P57951
|
FINISHED |
| Object | about 35 km southeast |
—
|
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 35 km southeast | Statement: [Elbasan, approximateDistanceToTirana, about 35 km southeast]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateDistanceToTirana Context triple: [Elbasan, approximateDistanceToTirana, about 35 km southeast]
-
A.
distanceToTirana_km
chosen
Indicates the physical distance, measured in kilometers, between a given place and the city of Tirana.
-
B.
distanceToSarandë
Indicates the spatial distance between a given entity and the location of Sarandë.
-
C.
distanceToKorçë
Indicates the spatial distance between an entity and the location of Korçë.
-
D.
distanceFromPreveza
Indicates the spatial distance between a given entity or location and the place referred to as Preveza.
-
E.
distanceToSkopje_km
Indicates the physical distance, measured in kilometers, between a given place and the city of Skopje.
- 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_69d8dd0ad9088190a173b32657ae2e7a |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5f8a5dabc81908fffad811f177b03 |
completed | April 20, 2026, 9:57 a.m. |
| PD | Predicate disambiguation | batch_69e4b9bb158481909478ca2e06f3ba39 |
completed | April 19, 2026, 11:17 a.m. |
Created at: April 10, 2026, 12:07 p.m.