Triple
T31563165
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sonkajärvi |
E805325
|
entity |
| Predicate | distanceToKuopioApprox |
P203743
|
FINISHED |
| Object | 100 km |
—
|
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: 100 km | Statement: [Sonkajärvi, distanceToKuopioApprox, 100 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToKuopioApprox Context triple: [Sonkajärvi, distanceToKuopioApprox, 100 km]
-
A.
distanceToTurkuApproxKm
Indicates an approximate physical distance, measured in kilometers, between a given entity and the city of Turku.
-
B.
distanceToTurku
Indicates the spatial distance between a given entity’s location and the city of Turku.
-
C.
distanceToOulu_km
Indicates the physical distance, measured in kilometers, between an entity’s location and the city of Oulu.
-
D.
distanceToLappeenranta_km
Indicates the physical distance, measured in kilometers, between an entity and the city of Lappeenranta.
-
E.
distanceToHelsinki_km
Indicates the physical distance, measured in kilometers, between an entity’s location and the city of Helsinki.
- 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_69f348d2ee94819091918d1789398c29 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a01e27bb77c8190a7686b77a524f177 |
completed | May 11, 2026, 2:06 p.m. |
| PD | Predicate disambiguation | batch_6a01da2614788190b31db15539f5ba58 |
completed | May 11, 2026, 1:31 p.m. |
| PDg | Predicate description generation | batch_6a01e27af6488190af75efc2283d99b3 |
completed | May 11, 2026, 2:06 p.m. |
Created at: April 30, 2026, 10:16 p.m.