Triple
T5270919
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Imatra |
E119253
|
entity |
| Predicate | distanceToLappeenranta_km |
P62750
|
FINISHED |
| Object | about 37 |
—
|
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 37 | Statement: [Imatra, distanceToLappeenranta_km, about 37]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToLappeenranta_km Context triple: [Imatra, distanceToLappeenranta_km, about 37]
-
A.
distanceToHelsinki_km
Indicates the physical distance, measured in kilometers, between an entity’s location and the city of Helsinki.
-
B.
distanceToTurku
Indicates the spatial distance between a given entity’s location and the city of Turku.
-
C.
distanceFromUppsala_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Uppsala.
-
D.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
-
E.
distanceToPoznań_km
Indicates the physical distance, measured in kilometers, between an entity and the city of Poznań.
- 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_69bd446c38e081908cdaf113bdf86790 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7d5a23908190a24e79d1b29d6fcf |
completed | March 20, 2026, 5:01 p.m. |
| PD | Predicate disambiguation | batch_69bd77c71268819094f9f5203eed392d |
completed | March 20, 2026, 4:37 p.m. |
| PDg | Predicate description generation | batch_69bd7d5906d88190b805977e5a05767a |
completed | March 20, 2026, 5:01 p.m. |
Created at: March 20, 2026, 1:51 p.m.