Triple
T8056466
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trnava |
E188013
|
entity |
| Predicate | distanceFromBratislava_km |
P80793
|
FINISHED |
| Object | approximately 47 |
—
|
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 47 | Statement: [Trnava, distanceFromBratislava_km, approximately 47]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromBratislava_km Context triple: [Trnava, distanceFromBratislava_km, approximately 47]
-
A.
distanceFromPragueKmApprox
Indicates an approximate distance, measured in kilometers, between a given entity and the city of Prague.
-
B.
distanceToŽilina_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Žilina.
-
C.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
-
D.
distanceFromLjubljana
Indicates the spatial distance between an entity and the city of Ljubljana.
-
E.
distanceToZakopane_km
Indicates the distance, measured in kilometers, between a given place and Zakopane.
- 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_69ca82b2f68881908c50560697e210da |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3fa26fb88190b6799bddeb68ed78 |
completed | March 31, 2026, 3:29 a.m. |
| PD | Predicate disambiguation | batch_69cb049a1b9c8190811c396421ebf9c9 |
completed | March 30, 2026, 11:17 p.m. |
| PDg | Predicate description generation | batch_69cb14bcbbc0819094a98e7ffffb7a40 |
completed | March 31, 2026, 12:26 a.m. |
Created at: March 30, 2026, 5:25 p.m.