Triple
T16196620
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lysebotn |
E393076
|
entity |
| Predicate | hasPort |
P35
|
FINISHED |
| Object | Lysebotn quay |
E393076
|
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: Lysebotn quay | Statement: [Lysebotn, hasPort, Lysebotn quay]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lysebotn quay Context triple: [Lysebotn, hasPort, Lysebotn quay]
-
A.
Lysebotn
chosen
Lysebotn is a small village at the innermost end of Norway’s Lysefjord, known as a gateway to famous hiking destinations like Kjerag and Preikestolen.
-
B.
Skibotn
Skibotn is a small village in northern Norway known for its clear skies and role as a center for astronomical observations.
-
C.
Enebakk
Enebakk is a rural municipality in Viken county, Norway, known for its forests, lakes, and proximity to the Oslo metropolitan area.
-
D.
Lessebo
Lessebo is a small locality and municipality in southern Sweden known for its traditional paper mill and glassmaking heritage.
-
E.
Namdalseid
Namdalseid is a former rural municipality in Trøndelag county, Norway, known for its forests, agriculture, and coastal landscape along the Namsenfjorden.
- 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_69d87f1e49ac8190a311b54d32990576 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e222dace848190b1a98e47333b922b |
completed | April 17, 2026, 12:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffff0f352081908324783743e47029 |
completed | May 10, 2026, 3:44 a.m. |
Created at: April 10, 2026, 5:02 a.m.