Triple
T14413056
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Riihimäki |
E357378
|
entity |
| Predicate | distanceToTampere |
P114155
|
FINISHED |
| Object | approximately 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: approximately 100 km | Statement: [Riihimäki, distanceToTampere, approximately 100 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToTampere Context triple: [Riihimäki, distanceToTampere, approximately 100 km]
-
A.
distanceToTurku
Indicates the spatial distance between a given entity’s location and the city of Turku.
-
B.
distanceToLappeenranta_km
Indicates the physical distance, measured in kilometers, between an entity and the city of Lappeenranta.
-
C.
distanceToHelsinki_km
Indicates the physical distance, measured in kilometers, between an entity’s location and the city of Helsinki.
-
D.
distanceToOulu_km
Indicates the physical distance, measured in kilometers, between an entity’s location and the city of Oulu.
-
E.
distanceFromTallinn
Indicates the measured distance between a given place or object and the city of Tallinn.
- 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_69d82793421c8190861eb0e673b085de |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de90cb3c708190822f5506ebf7ee9d |
completed | April 14, 2026, 7:08 p.m. |
| PD | Predicate disambiguation | batch_69de5c30467881908e770e3940295641 |
completed | April 14, 2026, 3:24 p.m. |
| PDg | Predicate description generation | batch_69de5fb4de14819092acdecbd201d672 |
completed | April 14, 2026, 3:39 p.m. |
Created at: April 10, 2026, 1:17 a.m.