Triple
T5652417
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ullensaker |
E124535
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object |
Nannestad
Nannestad is a rural municipality in Viken county, Norway, known for its agricultural landscape and proximity to Oslo Airport Gardermoen.
|
E570237
|
NE FINISHED |
How this triple was built (4 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: Nannestad | Statement: [Ullensaker, borderedBy, Nannestad]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nannestad Context triple: [Ullensaker, borderedBy, Nannestad]
-
A.
Gaustad
Gaustad is a district in Oslo, Norway, known for hosting major academic and research institutions, including parts of the University of Oslo campus.
-
B.
Rakkestad
Rakkestad is a rural municipality in Viken county, southeastern Norway, known for its agriculture and forests.
-
C.
Ringerike
Ringerike is a historic district and municipality in southeastern Norway known for its rich Viking-age heritage and distinctive cultural traditions.
-
D.
Grebbestad
Grebbestad is a coastal fishing village and popular tourist destination in Tanum Municipality on Sweden’s west coast, known for its seafood and picturesque archipelago.
-
E.
Hemnes
Hemnes is a municipality in Nordland county, Norway, known for its mountainous landscapes, fjords, and outdoor recreation opportunities.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Nannestad Triple: [Ullensaker, borderedBy, Nannestad]
Generated description
Nannestad is a rural municipality in Viken county, Norway, known for its agricultural landscape and proximity to Oslo Airport Gardermoen.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nannestad Target entity description: Nannestad is a rural municipality in Viken county, Norway, known for its agricultural landscape and proximity to Oslo Airport Gardermoen.
-
A.
Gaustad
Gaustad is a district in Oslo, Norway, known for hosting major academic and research institutions, including parts of the University of Oslo campus.
-
B.
Rakkestad
Rakkestad is a rural municipality in Viken county, southeastern Norway, known for its agriculture and forests.
-
C.
Ringerike
Ringerike is a historic district and municipality in southeastern Norway known for its rich Viking-age heritage and distinctive cultural traditions.
-
D.
Grebbestad
Grebbestad is a coastal fishing village and popular tourist destination in Tanum Municipality on Sweden’s west coast, known for its seafood and picturesque archipelago.
-
E.
Hemnes
Hemnes is a municipality in Nordland county, Norway, known for its mountainous landscapes, fjords, and outdoor recreation opportunities.
- F. None of above. chosen
Provenance (5 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_69c00825df388190a58742fa9b1aa33d |
completed | March 22, 2026, 3:17 p.m. |
| NER | Named-entity recognition | batch_69c022d8a2588190b10de59edbc8841f |
completed | March 22, 2026, 5:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c1351fb4a88190bb12f3a5f8cd92ac |
completed | March 23, 2026, 12:42 p.m. |
| NEDg | Description generation | batch_69c1369fb3648190874e7bbe4a6fd737 |
completed | March 23, 2026, 12:48 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c136fad48881908b449b8b411c3fc8 |
completed | March 23, 2026, 12:50 p.m. |
Created at: March 22, 2026, 3:42 p.m.