Triple
T13798033
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hamar, Norway |
E331565
|
entity |
| Predicate | hasHarbourOn |
P3007
|
FINISHED |
| Object | Lake 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: Lake Mjøsa | Statement: [Hamar, Norway, hasHarbourOn, Lake Mjøsa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lake Mjøsa Context triple: [Hamar, Norway, hasHarbourOn, Lake 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.
Lake Norsjø
Lake Norsjø is a large inland lake in Telemark, Norway, known as an important link in the Telemark Canal and a popular area for boating and recreation.
-
C.
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.
-
D.
Sjusjøen lake
Sjusjøen lake is a scenic freshwater lake in Norway, known for its surrounding cross-country skiing terrain and popular holiday cabins.
-
E.
Heddalsvatnet
Heddalsvatnet is a lake in Telemark, Norway, known as part of the Telemark waterway system and surrounded by forested hills and rural landscapes.
- 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_69d81c58feb08190a77bca8bf7d6d20f |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de025be1f08190aac525d72d7dc0c3 |
completed | April 14, 2026, 9:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7b086d6d48190b823ed0a4403fbc5 |
completed | May 3, 2026, 8:31 p.m. |
Created at: April 9, 2026, 10:11 p.m.