Triple
T37889566
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harkány |
E945088
|
entity |
| Predicate | distanceToPécs |
P204457
|
FINISHED |
| Object | approximately 25 km south |
—
|
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 25 km south | Statement: [Harkány, distanceToPécs, approximately 25 km south]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToPécs Context triple: [Harkány, distanceToPécs, approximately 25 km south]
-
A.
distanceToKecskemét_km
Indicates the physical distance, measured in kilometers, from a given location to Kecskemét.
-
B.
distanceToSopron_km
Indicates the physical distance, measured in kilometers, between a given place or object and the city of Sopron.
-
C.
distanceToGyőr_km
Indicates the physical distance, measured in kilometers, between an entity and the city of Győr.
-
D.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
-
E.
distanceToSzeged_km
Indicates the physical distance, measured in kilometers, between an entity and the city of Szeged.
- 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_69f76ef02668819089e7940c4001af5e |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037cae084081909004d77514c5f286 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a192a008190a9917688a9e804f4 |
completed | May 12, 2026, 7:06 p.m. |
| PDg | Predicate description generation | batch_6a037c84ecbc81908232e5215355f43b |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:19 p.m.