Triple
T12078207
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kon-Tiki (2012 film) |
E287605
|
entity |
| Predicate | musicBy |
P1952
|
FINISHED |
| Object | Johan Söderqvist |
E257038
|
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: Johan Söderqvist | Statement: [Kon-Tiki (2012 film), musicBy, Johan Söderqvist]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Johan Söderqvist Context triple: [Kon-Tiki (2012 film), musicBy, Johan Söderqvist]
-
A.
Johan Söderqvist
chosen
Johan Söderqvist is a Swedish film composer known for his atmospheric and emotionally nuanced scores for Scandinavian and international cinema.
-
B.
Göran Sonnevi
Göran Sonnevi is a Swedish poet renowned for his intellectually dense, politically engaged, and formally experimental poetry.
-
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.
Göran Andersson
Göran Andersson is a Swedish academic and engineer known for his contributions to electric power systems and energy technology.
-
E.
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.
- 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_69d6ab4846e081908ee7bbd66a6d3459 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9045e81f88190be2b1aabd93f077c |
completed | April 10, 2026, 2:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f5f66301f081909697f9dd444a099e |
completed | May 2, 2026, 1:04 p.m. |
Created at: April 8, 2026, 9:48 p.m.