Triple
T22613804
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gevelsberg |
E566784
|
entity |
| Predicate | distanceToWuppertal |
P148934
|
FINISHED |
| Object | about 15 km east |
—
|
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 15 km east | Statement: [Gevelsberg, distanceToWuppertal, about 15 km east]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToWuppertal Context triple: [Gevelsberg, distanceToWuppertal, about 15 km east]
-
A.
distanceToWiesbaden
Indicates the spatial distance between a given entity or location and the city of Wiesbaden.
-
B.
distanceToDortmund
Indicates the spatial distance between a given entity’s location and the city of Dortmund.
-
C.
distanceFromHagenHbf_km
Indicates the distance, measured in kilometers, between a given location and Hagen Hauptbahnhof (Hagen central railway station).
-
D.
distanceToKoblenz
Indicates the spatial distance between a given entity and the location of Koblenz.
-
E.
distanceToFrankfurtHbf
Indicates the spatial distance between a given location and Frankfurt Hauptbahnhof (Frankfurt Hbf).
- 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_69e245884860819081046ce07d5872c4 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f167ec03c48190b55394b7296f48e5 |
completed | April 29, 2026, 2:07 a.m. |
| PD | Predicate disambiguation | batch_69ee62855558819080da946c7b35a160 |
completed | April 26, 2026, 7:07 p.m. |
| PDg | Predicate description generation | batch_69ee8841e9cc81908d23b34215e3be71 |
completed | April 26, 2026, 9:48 p.m. |
Created at: April 17, 2026, 2:58 p.m.