Triple
T14987632
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ödeen |
E373745
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Kjell Ödeen
Kjell Ödeen was a Swedish speed skater who competed internationally in the mid-20th century.
|
E1143845
|
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: Kjell Ödeen | Statement: [Ödeen, hasNotableBearer, Kjell Ödeen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kjell Ödeen Context triple: [Ödeen, hasNotableBearer, Kjell Ödeen]
-
A.
Kjell Ödeen
Kjell Ödeen was a Swedish architect best known for designing major public buildings such as the Scandinavium arena in Gothenburg.
-
B.
Kjell Jansson
Kjell Jansson is a Swedish politician and member of the Moderate Party who has served in the Riksdag.
-
C.
Björn Waldegård
Björn Waldegård was a Swedish rally driver and the inaugural World Rally Championship drivers’ title winner, renowned for his success with multiple manufacturers during the 1970s and 1980s.
-
D.
Stellan Bengtsson
Stellan Bengtsson is a Swedish table tennis legend, renowned as the first Swede to win the men’s singles title at the World Table Tennis Championships in 1971.
-
E.
Kjell-Åke Andersson
Kjell-Åke Andersson is a Swedish football executive best known for his leadership role at the professional club Östersunds FK.
- 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: Kjell Ödeen Triple: [Ödeen, hasNotableBearer, Kjell Ödeen]
Generated description
Kjell Ödeen was a Swedish speed skater who competed internationally in the mid-20th century.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kjell Ödeen Target entity description: Kjell Ödeen was a Swedish speed skater who competed internationally in the mid-20th century.
-
A.
Kjell Ödeen
Kjell Ödeen was a Swedish architect best known for designing major public buildings such as the Scandinavium arena in Gothenburg.
-
B.
Kjell Jansson
Kjell Jansson is a Swedish politician and member of the Moderate Party who has served in the Riksdag.
-
C.
Björn Waldegård
Björn Waldegård was a Swedish rally driver and the inaugural World Rally Championship drivers’ title winner, renowned for his success with multiple manufacturers during the 1970s and 1980s.
-
D.
Stellan Bengtsson
Stellan Bengtsson is a Swedish table tennis legend, renowned as the first Swede to win the men’s singles title at the World Table Tennis Championships in 1971.
-
E.
Kjell-Åke Andersson
Kjell-Åke Andersson is a Swedish football executive best known for his leadership role at the professional club Östersunds FK.
- 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_69d85ccc84388190aa151e5173370c8d |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded7007588819095bb1de029a6f2eb |
completed | April 15, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fed31c0ab08190814c41d852f9f523 |
completed | May 9, 2026, 6:24 a.m. |
| NEDg | Description generation | batch_69fed4d497a481909cee1da86e6a6ab5 |
completed | May 9, 2026, 6:31 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fed73158048190b0ae618512b24eb9 |
completed | May 9, 2026, 6:41 a.m. |
Created at: April 10, 2026, 2:53 a.m.