Triple
T30301333
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Langenlois |
E770658
|
entity |
| Predicate | distanceToKremsAnDerDonau_km |
P203068
|
FINISHED |
| Object | approximately 10 |
—
|
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 10 | Statement: [Langenlois, distanceToKremsAnDerDonau_km, approximately 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToKremsAnDerDonau_km Context triple: [Langenlois, distanceToKremsAnDerDonau_km, approximately 10]
-
A.
distanceToEisenstadt_km
Indicates the physical distance, measured in kilometers, between a given place and Eisenstadt.
-
B.
distanceToSopron_km
Indicates the physical distance, measured in kilometers, between a given place or object and the city of Sopron.
-
C.
distanceFromVienna
Indicates the spatial distance between a given entity’s location and the city of Vienna.
-
D.
distanceFromSalzburg
Indicates the spatial distance between a given entity and the city of Salzburg.
-
E.
distanceToGyőr_km
Indicates the physical distance, measured in kilometers, between an entity and the city of Győr.
- 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_69f224881b948190b8c4921b250a44a3 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_6a011c64ff18819099195701bbecbb20 |
completed | May 11, 2026, 12:01 a.m. |
| PD | Predicate disambiguation | batch_6a011b7f7a508190b2ebb518fb7a2fe9 |
completed | May 10, 2026, 11:57 p.m. |
| PDg | Predicate description generation | batch_6a011c645b68819092ccbb14c1a4c454 |
completed | May 11, 2026, 12:01 a.m. |
Created at: April 29, 2026, 7:48 p.m.