Triple
T15550549
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hedmarken |
E370730
|
entity |
| Predicate | hasLake |
P1025
|
FINISHED |
| Object | Mjøsa |
E65750
|
NE 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: Mjøsa | Statement: [Hedmarken, hasLake, Mjøsa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mjøsa Context triple: [Hedmarken, hasLake, Mjøsa]
-
A.
Mjøsa Lake
chosen
Mjøsa Lake is Norway’s largest lake, located in the southeastern part of the country and known for its scenic surroundings and historic towns along its shores.
-
B.
Breiddalsvatnet
Breiddalsvatnet is a lake located in Skjåk municipality in Innlandet county, Norway, known for its mountainous surroundings and role in local outdoor recreation.
-
C.
Rembesdalsvatnet
Rembesdalsvatnet is a mountain lake in Ulvik municipality in Vestland county, western Norway, known for its scenic setting near the Hardangerjøkulen glacier.
-
D.
Djupvatnet
Djupvatnet is a high-altitude lake in Norway’s Møre og Romsdal county, known for its scenic mountain surroundings and proximity to popular tourist routes.
-
E.
Øymarksjøen
Øymarksjøen is a lake in southeastern Norway known for its forested surroundings, recreational fishing, and role in the local waterway system near the Swedish border.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d85cc6cf40819091f4a5facee1ebe6 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04a9551288190a583e8291c35f521 |
completed | April 16, 2026, 2:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffb0319f248190a37c9afa09c32428 |
completed | May 9, 2026, 10:07 p.m. |
Created at: April 10, 2026, 4:08 a.m.