Triple
T5065509
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Skien |
E114133
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Porsgrunn |
E116224
|
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: Porsgrunn | Statement: [Skien, locatedNear, Porsgrunn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Porsgrunn Context triple: [Skien, locatedNear, Porsgrunn]
-
A.
Porsgrunn
chosen
Porsgrunn is an industrial and port city in Telemark county in southeastern Norway, known for its porcelain production and location along the Telemark Canal.
-
B.
Skien
Skien is a historic city in southern Norway known as the birthplace of playwright Henrik Ibsen and as a regional commercial and industrial center.
-
C.
Sogndal
Sogndal is a village and municipality in Vestland county, Norway, known for its scenic fjord landscape, agriculture, and as a regional education and service center.
-
D.
Sarpsborg
Sarpsborg is a historic city and municipality in Viken county, Norway, known as one of the country’s oldest towns and an important industrial and administrative center in the Østfold region.
-
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_69bd443c0c8c81908663b77afb28e165 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7478f7988190bc0473e8af055147 |
completed | March 20, 2026, 4:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf88de71b481908ac7b13a3d1538db |
completed | March 22, 2026, 6:14 a.m. |
Created at: March 20, 2026, 1:38 p.m.