Triple
T3272114
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hamar |
E68670
|
entity |
| Predicate | hasNeighboringMunicipality |
P224
|
FINISHED |
| Object |
Løten
Løten is a rural municipality in Innlandet county, Norway, known for its agricultural landscape and historic connections to painter Edvard Munch.
|
E342715
|
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: Løten | Statement: [Hamar, hasNeighboringMunicipality, Løten]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Løten Context triple: [Hamar, hasNeighboringMunicipality, Løten]
-
A.
Lågen
Lågen is a major river in southeastern Norway that flows through the Gudbrandsdalen valley before joining the Mjøsa lake.
-
B.
Tunevannet
Tunevannet is a lake and recreational area near Sarpsborg in Østfold, Norway, known for outdoor activities such as swimming, fishing, and hiking.
-
C.
Gjallarhorn
Gjallarhorn is the resounding horn of the god Heimdall in Norse mythology, famously used to signal the onset of Ragnarök.
-
D.
Berg en Dal
Berg en Dal is a Dutch municipality in the province of Gelderland, known for its hilly landscape, forests, and proximity to the city of Nijmegen.
-
E.
Kungsängen
Kungsängen is a locality in Stockholm County, Sweden, serving as the administrative and population center of Upplands-Bro Municipality.
- 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: Løten Triple: [Hamar, hasNeighboringMunicipality, Løten]
Generated description
Løten is a rural municipality in Innlandet county, Norway, known for its agricultural landscape and historic connections to painter Edvard Munch.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Løten Target entity description: Løten is a rural municipality in Innlandet county, Norway, known for its agricultural landscape and historic connections to painter Edvard Munch.
-
A.
Lågen
Lågen is a major river in southeastern Norway that flows through the Gudbrandsdalen valley before joining the Mjøsa lake.
-
B.
Tunevannet
Tunevannet is a lake and recreational area near Sarpsborg in Østfold, Norway, known for outdoor activities such as swimming, fishing, and hiking.
-
C.
Gjallarhorn
Gjallarhorn is the resounding horn of the god Heimdall in Norse mythology, famously used to signal the onset of Ragnarök.
-
D.
Berg en Dal
Berg en Dal is a Dutch municipality in the province of Gelderland, known for its hilly landscape, forests, and proximity to the city of Nijmegen.
-
E.
Kungsängen
Kungsängen is a locality in Stockholm County, Sweden, serving as the administrative and population center of Upplands-Bro Municipality.
- 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_69ad859b54f881909bf530d549caf2fd |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adaff6308881908886a44804a0bb09 |
completed | March 8, 2026, 5:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b28f0793e08190af55ee16e5091451 |
completed | March 12, 2026, 10:01 a.m. |
| NEDg | Description generation | batch_69b296709f84819094fe9db721a44f83 |
completed | March 12, 2026, 10:33 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b2d6c47e908190b5be5b33da358ade |
completed | March 12, 2026, 3:07 p.m. |
Created at: March 8, 2026, 3:10 p.m.