Triple
T4365586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oppland |
E98763
|
entity |
| Predicate | containsPart |
P35
|
FINISHED |
| Object |
Lunner
Lunner is a rural municipality in Innlandet county, Norway, known for its forests, lakes, and role as part of the Hadeland traditional district.
|
E433535
|
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: Lunner | Statement: [Oppland, containsPart, Lunner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lunner Context triple: [Oppland, containsPart, Lunner]
-
A.
Lontzen
Lontzen is a municipality in eastern Belgium, located in the country’s German-speaking region near the border with Germany.
-
B.
Relander
Relander is a Finnish surname most notably associated with Lauri Kristian Relander, the second President of Finland.
-
C.
Lorens
Lorens is a character from Paulo Coelho’s novel "Brida," serving as one of the key figures in the protagonist’s spiritual and personal journey.
-
D.
Luttig
Luttig is the surname of J. Michael Luttig, a prominent American conservative jurist and former federal appellate judge known for his influence on constitutional law and the judiciary.
-
E.
Hedlund
Hedlund is a surname most notably associated with American actor Garrett Hedlund, known for roles in films like "Tron: Legacy" and "Friday Night Lights."
- 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: Lunner Triple: [Oppland, containsPart, Lunner]
Generated description
Lunner is a rural municipality in Innlandet county, Norway, known for its forests, lakes, and role as part of the Hadeland traditional district.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lunner Target entity description: Lunner is a rural municipality in Innlandet county, Norway, known for its forests, lakes, and role as part of the Hadeland traditional district.
-
A.
Lontzen
Lontzen is a municipality in eastern Belgium, located in the country’s German-speaking region near the border with Germany.
-
B.
Relander
Relander is a Finnish surname most notably associated with Lauri Kristian Relander, the second President of Finland.
-
C.
Lorens
Lorens is a character from Paulo Coelho’s novel "Brida," serving as one of the key figures in the protagonist’s spiritual and personal journey.
-
D.
Luttig
Luttig is the surname of J. Michael Luttig, a prominent American conservative jurist and former federal appellate judge known for his influence on constitutional law and the judiciary.
-
E.
Hedlund
Hedlund is a surname most notably associated with American actor Garrett Hedlund, known for roles in films like "Tron: Legacy" and "Friday Night Lights."
- 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_69b3454c772081908e20173e379e8ebe |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35200263081909bb326a4d7a8db99 |
completed | March 12, 2026, 11:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5dbcbbd1881908eb9f0ea6b2fe16b |
completed | March 14, 2026, 10:06 p.m. |
| NEDg | Description generation | batch_69b5dcf36dfc8190847925dbed92c059 |
completed | March 14, 2026, 10:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5ddad45b8819082ac7a3a9c5f2f07 |
completed | March 14, 2026, 10:14 p.m. |
Created at: March 12, 2026, 11:17 p.m.