Triple
T10393910
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Göran |
E244957
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Göran Lennmarker
Göran Lennmarker is a Swedish politician known for his long service in the Riksdag and his work on foreign affairs and security policy.
|
E874735
|
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: Göran Lennmarker | Statement: [Göran, hasNotableBearer, Göran Lennmarker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Göran Lennmarker Context triple: [Göran, hasNotableBearer, Göran Lennmarker]
-
A.
Göran Månsson
Göran Månsson is a Swedish architect best known for designing Stockholm’s renowned Vasa Museum, which houses the 17th-century warship Vasa.
-
B.
Göran Sonnevi
Göran Sonnevi is a Swedish poet renowned for his intellectually dense, politically engaged, and formally experimental poetry.
-
C.
Torgny Segerstedt
Torgny Segerstedt was a Swedish philosopher and academic leader best known for serving as rector of Uppsala University and for his influence on higher education in Sweden.
-
D.
Hans Göran
Hans Göran is the given first name of Göran Persson, the former Prime Minister of Sweden.
-
E.
Göran Hägglund
Göran Hägglund is a Swedish Christian Democrat politician who served as Minister for Health and Social Affairs and leader of the Christian Democrats.
- 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: Göran Lennmarker Triple: [Göran, hasNotableBearer, Göran Lennmarker]
Generated description
Göran Lennmarker is a Swedish politician known for his long service in the Riksdag and his work on foreign affairs and security policy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Göran Lennmarker Target entity description: Göran Lennmarker is a Swedish politician known for his long service in the Riksdag and his work on foreign affairs and security policy.
-
A.
Göran Månsson
Göran Månsson is a Swedish architect best known for designing Stockholm’s renowned Vasa Museum, which houses the 17th-century warship Vasa.
-
B.
Göran Sonnevi
Göran Sonnevi is a Swedish poet renowned for his intellectually dense, politically engaged, and formally experimental poetry.
-
C.
Torgny Segerstedt
Torgny Segerstedt was a Swedish philosopher and academic leader best known for serving as rector of Uppsala University and for his influence on higher education in Sweden.
-
D.
Hans Göran
Hans Göran is the given first name of Göran Persson, the former Prime Minister of Sweden.
-
E.
Göran Hägglund
Göran Hägglund is a Swedish Christian Democrat politician who served as Minister for Health and Social Affairs and leader of the Christian Democrats.
- 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_69d381b5116081908d85227bab6d3c0c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e9b795fc8190aa50ce3c7360ff83 |
completed | April 7, 2026, 11:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d95e4aef148190be58486605f85f77 |
completed | April 10, 2026, 8:32 p.m. |
| NEDg | Description generation | batch_69d95f508b6481909405f0404246c69e |
completed | April 10, 2026, 8:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d9600a29808190af583d2fd696ec6a |
completed | April 10, 2026, 8:39 p.m. |
Created at: April 6, 2026, 12:06 p.m.