Triple
T33302693
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Falkenlust Palace |
E852627
|
entity |
| Predicate | distanceToAugustusburg |
P206414
|
FINISHED |
| Object | approximately 2.5 km |
—
|
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 2.5 km | Statement: [Falkenlust Palace, distanceToAugustusburg, approximately 2.5 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToAugustusburg Context triple: [Falkenlust Palace, distanceToAugustusburg, approximately 2.5 km]
-
A.
distanceToAugsburg_km
Indicates the physical distance, measured in kilometers, between a given place and Augsburg.
-
B.
distanceToBurghausen
Indicates the measured spatial distance between a given entity and the location of Burghausen.
-
C.
distanceToPassau
Indicates the spatial distance between a given entity or location and the city of Passau.
-
D.
distanceToRegensburg
Indicates the spatial distance between a given entity and the city of Regensburg.
-
E.
distanceToRosenheim
Indicates the spatial distance between a given entity and the location Rosenheim.
- 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_69f34966ed4c81908dc9dda82d8c7fe3 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a037c9141dc819098d7fcc36e69882c |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379f338b881908e5593e45d764f4d |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c7fb9f88190b384b1b68200aef0 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 1, 2026, 1:33 a.m.