Triple
T12422406
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Erich Bergen |
E296807
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Bergen
Bergen is a historic coastal city in western Norway known for its surrounding mountains, vibrant harbor, and role as a former Hanseatic trading hub.
|
E74082
|
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: Bergen | Statement: [Erich Bergen, familyName, Bergen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bergen Context triple: [Erich Bergen, familyName, Bergen]
-
A.
Bergen
Bergen is Norway's second-largest city, renowned for its historic harbor, surrounding mountains and fjords, and role as a former Hanseatic trading hub.
-
B.
Bergen
Bergen is a city in western Germany, historically notable as the site of the 1759 Battle of Bergen during the Seven Years' War.
-
C.
Bergens
The Bergens are a race of gloomy, troll-eating creatures who serve as the primary villains in the animated film "Trolls."
-
D.
Trondheim
Trondheim is a historic Norwegian city in Trøndelag county, known for its medieval Nidaros Cathedral and role as a former capital of Norway.
-
E.
Stavanger
Stavanger is a coastal city in southwestern Norway known for its oil industry hub status, historic wooden houses, and proximity to natural attractions like the Lysefjord and Preikestolen.
- 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: Bergen Triple: [Erich Bergen, familyName, Bergen]
Generated description
Bergen is a historic coastal city in western Norway known for its surrounding mountains, vibrant harbor, and role as a former Hanseatic trading hub.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bergen Target entity description: Bergen is a historic coastal city in western Norway known for its surrounding mountains, vibrant harbor, and role as a former Hanseatic trading hub.
-
A.
Bergen
chosen
Bergen is Norway's second-largest city, renowned for its historic harbor, surrounding mountains and fjords, and role as a former Hanseatic trading hub.
-
B.
Bergen
Bergen is a city in western Germany, historically notable as the site of the 1759 Battle of Bergen during the Seven Years' War.
-
C.
Bergens
The Bergens are a race of gloomy, troll-eating creatures who serve as the primary villains in the animated film "Trolls."
-
D.
Trondheim
Trondheim is a historic Norwegian city in Trøndelag county, known for its medieval Nidaros Cathedral and role as a former capital of Norway.
-
E.
Stavanger
Stavanger is a coastal city in southwestern Norway known for its oil industry hub status, historic wooden houses, and proximity to natural attractions like the Lysefjord and Preikestolen.
- F. None of above.
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_69d6ada0640c81908c061d7fb3d47786 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94d702b1481909db5f5bed6292ce0 |
completed | April 10, 2026, 7:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f69b8077d081908e226e5bf856bcbf |
completed | May 3, 2026, 12:49 a.m. |
| NEDg | Description generation | batch_69f69c8469548190b05d8fa010e0ca13 |
completed | May 3, 2026, 12:53 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f69d4ef7988190890f8a62280aa673 |
completed | May 3, 2026, 12:56 a.m. |
Created at: April 8, 2026, 9:55 p.m.