Triple
T1305567
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brussels South Charleroi Airport |
E27867
|
entity |
| Predicate | cityDistanceFromBrussels_km |
P26827
|
FINISHED |
| Object | 46 |
—
|
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: 46 | Statement: [Brussels South Charleroi Airport, cityDistanceFromBrussels_km, 46]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cityDistanceFromBrussels_km Context triple: [Brussels South Charleroi Airport, cityDistanceFromBrussels_km, 46]
-
A.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
-
B.
approximateDistanceKm
Indicates the estimated distance between two entities measured in kilometers, typically with some degree of inaccuracy or approximation.
-
C.
distanceFromOslo
Indicates the spatial distance between a given entity’s location and the city of Oslo.
-
D.
distanceFromParisCenter
Indicates the measured distance between a given location and the central point of Paris.
-
E.
distanceFromCapital
Indicates the measured distance between a given location and the capital city of its corresponding region or country.
- F. None of above. chosen
Provenance (4 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_69a496d7d83481908f83085854e51328 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c13524d481909e8f5bb2ab91f6e4 |
completed | March 1, 2026, 10:44 p.m. |
| PD | Predicate disambiguation | batch_69a4bee8544c8190874efd9bae9bccf9 |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4bf60545c8190901ccfb2cb7c4b41 |
completed | March 1, 2026, 10:36 p.m. |
Created at: March 1, 2026, 7:51 p.m.