Triple
T17249145
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kiskunfélegyháza |
E418702
|
entity |
| Predicate | distanceToSzeged_km |
P126564
|
FINISHED |
| Object | approximately 60 |
—
|
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 60 | Statement: [Kiskunfélegyháza, distanceToSzeged_km, approximately 60]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToSzeged_km Context triple: [Kiskunfélegyháza, distanceToSzeged_km, approximately 60]
-
A.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
-
B.
distanceToPoznań_km
Indicates the physical distance, measured in kilometers, between an entity and the city of Poznań.
-
C.
distanceToElbląg_km
Indicates the physical distance, measured in kilometers, between an entity and the location Elbląg.
-
D.
distanceToŽilina_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Žilina.
-
E.
distanceToKoblenz
Indicates the spatial distance between a given entity and the location of Koblenz.
- 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_69d886d9ab108190b70edd8d17aa1204 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42e2636f48190b29548ff80402bef |
completed | April 19, 2026, 1:21 a.m. |
| PD | Predicate disambiguation | batch_69e3832553ac819091aa917c84f755b6 |
completed | April 18, 2026, 1:12 p.m. |
| PDg | Predicate description generation | batch_69e3873f62108190966c4e741ebd548d |
completed | April 18, 2026, 1:29 p.m. |
Created at: April 10, 2026, 5:39 a.m.