Triple
T5290485
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jessheim |
E119728
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Ullensaker |
E124535
|
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: Ullensaker | Statement: [Jessheim, locatedIn, Ullensaker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ullensaker Context triple: [Jessheim, locatedIn, Ullensaker]
-
A.
Ullensaker
chosen
Ullensaker is a municipality in Viken county, Norway, best known for hosting Oslo Airport, Gardermoen, the country’s main international airport.
-
B.
Sinsen
Sinsen is a neighborhood and major transport hub in Oslo, Norway, known for its busy traffic interchange and public transit connections.
-
C.
Bærum
Bærum is a wealthy suburban municipality just west of Oslo, Norway, known for its high standard of living and residential communities.
-
D.
Drammen
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.
-
E.
Håvik
Håvik is a small coastal village located in Karmøy municipality in Rogaland county, southwestern Norway.
- 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_69bd446de5648190b313a90bd96730d2 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd84eac7b88190900142bd1310c0fd |
completed | March 20, 2026, 5:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf3a8c43988190814e3b2cca509f15 |
completed | March 22, 2026, 12:40 a.m. |
Created at: March 20, 2026, 1:52 p.m.