Triple
T10393911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Göran |
E244957
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Göran Hägglund |
E719887
|
NE FINISHED |
How this triple was built (2 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 Hägglund | Statement: [Göran, hasNotableBearer, Göran Hägglund]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Göran Hägglund Context triple: [Göran, hasNotableBearer, Göran Hägglund]
-
A.
Göran Hägglund
chosen
Göran Hägglund is a Swedish Christian Democrat politician who served as Minister for Health and Social Affairs and leader of the Christian Democrats.
-
B.
Göran Gustafsson
Göran Gustafsson was a Swedish entrepreneur and philanthropist known for his significant contributions to scientific research funding.
-
C.
Göran Månsson
Göran Månsson is a Swedish architect best known for designing Stockholm’s renowned Vasa Museum, which houses the 17th-century warship Vasa.
-
D.
Stig Strömholm
Stig Strömholm is a Swedish legal scholar, author, and academic leader who served as a prominent rector of Uppsala University.
-
E.
Göran Malmqvist
Göran Malmqvist was a Swedish sinologist, literary historian, translator, and member of the Swedish Academy known for his influential work on Chinese language and literature.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d96b1d5b388190841ed0df2145ad7a |
completed | April 10, 2026, 9:26 p.m. |
Created at: April 6, 2026, 12:06 p.m.