Triple
T653455
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sucha Beskidzka |
E11393
|
entity |
| Predicate | distanceToKraków |
P15547
|
FINISHED |
| Object | about 70 km southwest |
—
|
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 70 km southwest | Statement: [Sucha Beskidzka, distanceToKraków, about 70 km southwest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToKraków Context triple: [Sucha Beskidzka, distanceToKraków, about 70 km southwest]
-
A.
distanceToKraków_km
chosen
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Kraków.
-
B.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
-
C.
distanceToDresden
Indicates the spatial distance between a given entity or location and the city of Dresden.
-
D.
distanceToBerlin
Indicates the spatial distance between a given entity’s location and the city of Berlin.
-
E.
distanceToLeipzig
Indicates the spatial distance between a given entity’s location and the city of Leipzig.
- 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_69a493266a2881909daf4c40f719dee8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49f4a660c8190b887cb4da01ef7ae |
completed | March 1, 2026, 8:19 p.m. |
| PD | Predicate disambiguation | batch_69a49d1001088190aa7ca3c8f2ad0e32 |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:36 p.m.