Triple
T5729714
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norwegian Defence Research Establishment |
E126350
|
entity |
| Predicate | headquartersLocation |
P62
|
FINISHED |
| Object |
Kjeller
Kjeller is a research-focused village in Lillestrøm, Norway, known as a major hub for defense, aviation, and technology institutions.
|
E592821
|
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: Kjeller | Statement: [Norwegian Defence Research Establishment, headquartersLocation, Kjeller]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kjeller Context triple: [Norwegian Defence Research Establishment, headquartersLocation, Kjeller]
-
A.
Steinkjer
Steinkjer is a town and municipality in central Norway that serves as an important regional center and administrative hub in Trøndelag county.
-
B.
Kongsberg
Kongsberg is a Norwegian town known for its historic silver mines and its modern high-tech and defense industries.
-
C.
Lysaker
Lysaker is a key transport and business hub in the western part of the Oslo metropolitan area in Norway, featuring a major railway and commuter center.
-
D.
Notodden
Notodden is a town and municipality in Vestfold og Telemark county, Norway, known for its industrial heritage and annual blues festival.
-
E.
Trysil
Trysil is a Norwegian municipality renowned for its large alpine ski resort and extensive 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: Kjeller Triple: [Norwegian Defence Research Establishment, headquartersLocation, Kjeller]
Generated description
Kjeller is a research-focused village in Lillestrøm, Norway, known as a major hub for defense, aviation, and technology institutions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kjeller Target entity description: Kjeller is a research-focused village in Lillestrøm, Norway, known as a major hub for defense, aviation, and technology institutions.
-
A.
Steinkjer
Steinkjer is a town and municipality in central Norway that serves as an important regional center and administrative hub in Trøndelag county.
-
B.
Kongsberg
Kongsberg is a Norwegian town known for its historic silver mines and its modern high-tech and defense industries.
-
C.
Lysaker
Lysaker is a key transport and business hub in the western part of the Oslo metropolitan area in Norway, featuring a major railway and commuter center.
-
D.
Notodden
Notodden is a town and municipality in Vestfold og Telemark county, Norway, known for its industrial heritage and annual blues festival.
-
E.
Trysil
Trysil is a Norwegian municipality renowned for its large alpine ski resort and extensive 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_69c0082f723881908ce8bb13a0c0f8b7 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c025303860819093e51f176babed71 |
completed | March 22, 2026, 5:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c64096580481909a253f26b9535d9f |
completed | March 27, 2026, 8:32 a.m. |
| NEDg | Description generation | batch_69c641e5436881908bdc5fc92c6718cc |
completed | March 27, 2026, 8:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c64244ada0819081c799d80e2f8619 |
completed | March 27, 2026, 8:39 a.m. |
Created at: March 22, 2026, 3:47 p.m.