Triple
T35921216
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frankfurt-Hahn Airport |
E1038889
|
entity |
| Predicate | distanceFromLuxembourg |
P40111
|
FINISHED |
| Object | approximately 120 km |
—
|
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 120 km | Statement: [Frankfurt-Hahn Airport, distanceFromLuxembourg, approximately 120 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromLuxembourg Context triple: [Frankfurt-Hahn Airport, distanceFromLuxembourg, approximately 120 km]
-
A.
distanceToLuxembourgCityKilometers
chosen
Indicates the physical distance, measured in kilometers, between the given entity and Luxembourg City.
-
B.
distanceToLuxembourgBorderKilometers
Indicates the distance, measured in kilometers, from a given location to the nearest point on the border of Luxembourg.
-
C.
distanceFromStrasbourg
Indicates the spatial distance between a given place or entity and the city of Strasbourg.
-
D.
distanceFromNiceByRoad_km
Indicates the length of the road route, in kilometers, from the city of Nice to the given location.
-
E.
distanceToEschSurAlzette
Indicates the measured distance between a given entity and the location Esch-sur-Alzette.
- 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_69f76e2320748190b7f5c4750d0cd0d3 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037ce70f54819082946dad8d380825 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a069e6c8190857b611fffb7b867 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:07 p.m.