Triple
T7392570
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rüsselsheim am Main |
E170536
|
entity |
| Predicate | distanceToFrankfurtAirport |
P56547
|
FINISHED |
| Object | approximately 12 kilometres |
—
|
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 12 kilometres | Statement: [Rüsselsheim am Main, distanceToFrankfurtAirport, approximately 12 kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToFrankfurtAirport Context triple: [Rüsselsheim am Main, distanceToFrankfurtAirport, approximately 12 kilometres]
-
A.
distanceToFrankfurtAirport_km
chosen
Indicates the physical distance, measured in kilometers, between a given location and Frankfurt Airport.
-
B.
distanceToBerlin
Indicates the spatial distance between a given entity’s location and the city of Berlin.
-
C.
distanceToStockholmArlandaAirport
Indicates the measured distance between a given location or entity and Stockholm Arlanda Airport.
-
D.
distanceToStuttgart
Indicates the measured distance between a given entity’s location and the city of Stuttgart.
-
E.
distanceToManchesterAirport
Indicates the spatial distance between a given location or entity and Manchester Airport.
- 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_69c68a5e2c9081909e713ce866e0060a |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f224790c819099ceb7c7ac8d00f6 |
completed | March 27, 2026, 9:09 p.m. |
| PD | Predicate disambiguation | batch_69c6f0309cc88190b55d278969400294 |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:09 p.m.