Triple
T6216552
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rogaland |
E139000
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
Bokn
Bokn is a small island municipality in southwestern Norway known for its coastal landscape and location in Rogaland county.
|
E577059
|
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: Bokn | Statement: [Rogaland, hasMunicipality, Bokn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bokn Context triple: [Rogaland, hasMunicipality, Bokn]
-
A.
Boknal
Boknal refers to the three hottest days of summer in South Korea, traditionally marked by eating stamina-boosting foods like samgyetang to combat the heat.
-
B.
BOK
BOK is the commonly used abbreviation for the Bank of Korea, South Korea’s central bank responsible for monetary policy and financial stability.
-
C.
Rjukan
Rjukan is a Norwegian industrial town in a deep valley in Telemark, known for its hydroelectric power heritage and World War II heavy water sabotage.
-
D.
Bøler
Bøler is a residential neighborhood in the Østensjø borough of Oslo, Norway, known for its post-war apartment blocks, green surroundings, and access to the Østmarka forest.
-
E.
Buk
Buk is a Soviet-designed, medium-range, surface-to-air missile system widely used for air defense by several countries, including Ukraine.
- 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: Bokn Triple: [Rogaland, hasMunicipality, Bokn]
Generated description
Bokn is a small island municipality in southwestern Norway known for its coastal landscape and location in Rogaland county.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bokn Target entity description: Bokn is a small island municipality in southwestern Norway known for its coastal landscape and location in Rogaland county.
-
A.
Boknal
Boknal refers to the three hottest days of summer in South Korea, traditionally marked by eating stamina-boosting foods like samgyetang to combat the heat.
-
B.
BOK
BOK is the commonly used abbreviation for the Bank of Korea, South Korea’s central bank responsible for monetary policy and financial stability.
-
C.
Rjukan
Rjukan is a Norwegian industrial town in a deep valley in Telemark, known for its hydroelectric power heritage and World War II heavy water sabotage.
-
D.
Bøler
Bøler is a residential neighborhood in the Østensjø borough of Oslo, Norway, known for its post-war apartment blocks, green surroundings, and access to the Østmarka forest.
-
E.
Buk
Buk is a Soviet-designed, medium-range, surface-to-air missile system widely used for air defense by several countries, including Ukraine.
- 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_69c008aecb0c81909984b48f733ce8ae |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c062a1eb3881908c7f735cf9c429ce |
completed | March 22, 2026, 9:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c20db4e0ac8190ba7bca1f9d8ac6df |
completed | March 24, 2026, 4:06 a.m. |
| NEDg | Description generation | batch_69c20ff2bb188190baf8a849efc15f87 |
completed | March 24, 2026, 4:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c21365f8f48190970555bd9593b5a4 |
completed | March 24, 2026, 4:30 a.m. |
Created at: March 22, 2026, 4:21 p.m.