Triple
T6074834
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Johan Fredrik Eckersberg |
E135373
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Drammen |
E105261
|
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: Drammen | Statement: [Johan Fredrik Eckersberg, placeOfBirth, Drammen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Drammen Context triple: [Johan Fredrik Eckersberg, placeOfBirth, Drammen]
-
A.
Drammen
chosen
Drammen is a city and municipality in southeastern Norway known for its riverside setting along the Drammenselva and its role as a regional commercial and transport hub.
-
B.
Bærum
Bærum is a wealthy suburban municipality just west of Oslo, Norway, known for its high standard of living and residential communities.
-
C.
Røyken
Røyken is a former municipality and suburban area in southeastern Norway, located along the Oslofjord and historically part of Buskerud county.
-
D.
Gjøvik
Gjøvik is a town and municipality in Innlandet county, Norway, known for its location along Lake Mjøsa and its mix of industrial heritage and modern sports and cultural facilities.
-
E.
Lysaker
Lysaker is a key transport and business hub in the western part of the Oslo metropolitan area in Norway, featuring a major railway and commuter center.
- 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_69c00879e8048190b690717d19c5bc03 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c0575d4ed481908eddc88e9b90e22f |
completed | March 22, 2026, 8:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c750fd986c819099dcf429008e8cdf |
completed | March 28, 2026, 3:54 a.m. |
Created at: March 22, 2026, 4:11 p.m.