Triple
T26396641
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mosonmagyaróvár |
E663578
|
entity |
| Predicate | distanceToGyőr_km |
P196170
|
FINISHED |
| Object | about 40 |
—
|
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 40 | Statement: [Mosonmagyaróvár, distanceToGyőr_km, about 40]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToGyőr_km Context triple: [Mosonmagyaróvár, distanceToGyőr_km, about 40]
-
A.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
-
B.
distanceToKecskemét_km
Indicates the physical distance, measured in kilometers, from a given location to Kecskemét.
-
C.
distanceToSzeged_km
Indicates the physical distance, measured in kilometers, between an entity and the city of Szeged.
-
D.
distanceFromBratislava_km
Indicates the distance, measured in kilometers, between a given entity’s location and the city of Bratislava.
-
E.
distanceToEisenstadt_km
Indicates the physical distance, measured in kilometers, between a given place and Eisenstadt.
- 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_69ee883823988190b418b111be28a44a |
completed | April 26, 2026, 9:48 p.m. |
| NER | Named-entity recognition | batch_69fe12a899d4819080d48423f32eace9 |
completed | May 8, 2026, 4:43 p.m. |
| PD | Predicate disambiguation | batch_69fe0d7f6aa08190a1d2dfc025d4e0dc |
completed | May 8, 2026, 4:21 p.m. |
| PDg | Predicate description generation | batch_69fe12a769c08190bc445d302d2e8f98 |
completed | May 8, 2026, 4:43 p.m. |
Created at: April 26, 2026, 11:29 p.m.