Triple
T30809021
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hechingen |
E784587
|
entity |
| Predicate | distanceFromStuttgart |
P49992
|
FINISHED |
| Object | about 60 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 60 kilometres | Statement: [Hechingen, distanceFromStuttgart, about 60 kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromStuttgart Context triple: [Hechingen, distanceFromStuttgart, about 60 kilometres]
-
A.
distanceToStuttgart
chosen
Indicates the measured distance between a given entity’s location and the city of Stuttgart.
-
B.
distanceToKarlsruhe
Indicates the spatial distance between a given entity and the location of Karlsruhe.
-
C.
distanceToMunich
Indicates the spatial distance between a given entity’s location and the city of Munich.
-
D.
distanceToFrankfurt
Indicates the spatial distance between a given location or entity and the city of Frankfurt.
-
E.
distanceFromDarmstadtHbf
Indicates the spatial distance between a given location and Darmstadt Hauptbahnhof (Darmstadt central railway station).
- 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_69f224b3a7ec819096939414d103e31e |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a015ff02814819094806517fc4c69fa |
completed | May 11, 2026, 4:49 a.m. |
| PD | Predicate disambiguation | batch_6a0154ddd3c48190b85f9f48731cfd8f |
completed | May 11, 2026, 4:02 a.m. |
Created at: April 29, 2026, 8:43 p.m.