Triple
T22827869
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Herlev |
E565708
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Copenhagen |
—
|
NE NERFINISHED |
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: Copenhagen | Statement: [Herlev, locatedNear, Copenhagen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Copenhagen Context triple: [Herlev, locatedNear, Copenhagen]
-
A.
Copenhagen
chosen
Copenhagen is the capital and largest city of Denmark, known for its historic architecture, vibrant cultural scene, and high quality of life.
-
B.
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.
-
C.
Odense
Odense is a historic Danish city on the island of Funen, best known as the birthplace of fairy-tale author Hans Christian Andersen and a cultural hub with museums, festivals, and a vibrant literary heritage.
-
D.
Hankø
Hankø is a small Norwegian island and resort area known for its sailing, summer tourism, and scenic coastal landscapes.
-
E.
Helsinge
Helsinge is a town in North Zealand, Denmark, known as a local commercial and transport hub connected by rail to nearby cities including Hillerød.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e24585ab1c81909b2b5065d15805d5 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17e2914188190be6cdbd8167cd806 |
completed | April 29, 2026, 3:42 a.m. |
Created at: April 17, 2026, 3:34 p.m.