Triple
T10394434
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Four Shades of Brown |
E244973
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object |
Henrik Schyffert
Henrik Schyffert is a Swedish comedian, actor, and director known for his influential work in television, film, and stand-up comedy.
|
E874736
|
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: Henrik Schyffert | Statement: [Four Shades of Brown, hasCastMember, Henrik Schyffert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Henrik Schyffert Context triple: [Four Shades of Brown, hasCastMember, Henrik Schyffert]
-
A.
Jorgen Holmboe
Jorgen Holmboe was a Norwegian-American meteorologist known for his contributions to dynamic meteorology and weather forecasting theory.
-
B.
Morten Ristorp
Morten Ristorp is a Danish songwriter and producer known for his work on international pop and R&B hits.
-
C.
Ole Christensen
Ole Christensen is a Danish mathematician known for his contributions to functional analysis and frame theory.
-
D.
Christian Møller
Christian Møller was a Danish theoretical physicist known for his contributions to quantum electrodynamics and the theory of relativity.
-
E.
Gunnar Wejke
Gunnar Wejke was a Swedish architect known for co-designing major public buildings, including the multi-purpose arena Scandinavium in Gothenburg.
- 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: Henrik Schyffert Triple: [Four Shades of Brown, hasCastMember, Henrik Schyffert]
Generated description
Henrik Schyffert is a Swedish comedian, actor, and director known for his influential work in television, film, and stand-up comedy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Henrik Schyffert Target entity description: Henrik Schyffert is a Swedish comedian, actor, and director known for his influential work in television, film, and stand-up comedy.
-
A.
Jorgen Holmboe
Jorgen Holmboe was a Norwegian-American meteorologist known for his contributions to dynamic meteorology and weather forecasting theory.
-
B.
Morten Ristorp
Morten Ristorp is a Danish songwriter and producer known for his work on international pop and R&B hits.
-
C.
Ole Christensen
Ole Christensen is a Danish mathematician known for his contributions to functional analysis and frame theory.
-
D.
Christian Møller
Christian Møller was a Danish theoretical physicist known for his contributions to quantum electrodynamics and the theory of relativity.
-
E.
Gunnar Wejke
Gunnar Wejke was a Swedish architect known for co-designing major public buildings, including the multi-purpose arena Scandinavium in Gothenburg.
- 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.