Triple
T14792903
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Friedberg, Hesse |
E347700
|
entity |
| Predicate | distanceToFrankfurtAmMain |
P88078
|
FINISHED |
| Object | about 30 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: about 30 kilometres | Statement: [Friedberg, Hesse, distanceToFrankfurtAmMain, about 30 kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToFrankfurtAmMain Context triple: [Friedberg, Hesse, distanceToFrankfurtAmMain, about 30 kilometres]
-
A.
distanceToFrankfurt
chosen
Indicates the spatial distance between a given location or entity and the city of Frankfurt.
-
B.
distanceToFrankfurtAirport_km
Indicates the physical distance, measured in kilometers, between a given location and Frankfurt Airport.
-
C.
distanceToBerlin
Indicates the spatial distance between a given entity’s location and the city of Berlin.
-
D.
distanceToMunich
Indicates the spatial distance between a given entity’s location and the city of Munich.
-
E.
distanceToStuttgart
Indicates the measured distance between a given entity’s location and the city of Stuttgart.
- 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_69d822ea8b7c819097dfadf3d45545e6 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69decd5ec43c8190ad7a10a556519bb0 |
completed | April 14, 2026, 11:27 p.m. |
| PD | Predicate disambiguation | batch_69de8c090d1081909b5a9bf437499d6c |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:31 a.m.