Triple
T4434732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | European route E134 |
E95620
|
entity |
| Predicate | easternTerminus |
P388
|
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: [European route E134, easternTerminus, Drammen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Drammen Context triple: [European route E134, easternTerminus, 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.
Lørenskog
Lørenskog is a suburban municipality in Viken county, Norway, located just east of Oslo and known for its residential areas and commercial centers.
- 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_69b3453ea2b48190a26f154b3b8fece5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35588e99881908fea7b71a33e2bb6 |
completed | March 13, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beba400cd88190a2348ec3ac6b711e |
completed | March 21, 2026, 3:33 p.m. |
Created at: March 12, 2026, 11:31 p.m.