Triple
T12707892
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Onsøy |
E303635
|
entity |
| Predicate | hasIsland |
P970
|
FINISHED |
| Object | Hankø |
E999619
|
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: Hankø | Statement: [Onsøy, hasIsland, Hankø]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hankø Context triple: [Onsøy, hasIsland, Hankø]
-
A.
Hankø
chosen
Hankø is a small Norwegian island and resort area known for its sailing, summer tourism, and scenic coastal landscapes.
-
B.
Helsingør
Helsingør is a historic coastal city in eastern Denmark, best known internationally as the setting of Shakespeare’s Hamlet (as Elsinore) and for its prominent Kronborg Castle overlooking the Øresund Strait.
-
C.
Copenhagen
Copenhagen is the capital and largest city of Denmark, known for its historic architecture, vibrant cultural scene, and high quality of life.
-
D.
Copenhagen
Copenhagen is a popular American smokeless tobacco (chewing tobacco/dip) brand known for its long history and strong presence in the U.S. market.
-
E.
Esbjerg
Esbjerg is a major Danish port city on the North Sea, known for its offshore oil and wind industry, maritime heritage, and role as a regional economic center in western Jutland.
- 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_69d7bdef90d48190b46b88270e780946 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9620663e881908d367170ed6d2c81 |
completed | April 10, 2026, 8:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f684e43424819080659ab152caae52 |
completed | May 2, 2026, 11:12 p.m. |
Created at: April 9, 2026, 5:23 p.m.