Triple
T21993442
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Goede tijden, slechte tijden |
E543144
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object |
Saskia van Houten
Saskia van Houten is a fictional character from the long-running Dutch soap opera "Goede tijden, slechte tijden."
|
E1517584
|
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: Saskia van Houten | Statement: [Goede tijden, slechte tijden, hasCharacter, Saskia van Houten]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saskia van Houten Context triple: [Goede tijden, slechte tijden, hasCharacter, Saskia van Houten]
-
A.
Julia van der Toorn
Julia van der Toorn is a Dutch singer who gained national fame after winning the talent show The Voice of Holland.
-
B.
Astrid Nienhuis
Astrid Nienhuis is a Dutch politician who serves as the mayor of the municipality of Heemstede in the Netherlands.
-
C.
Jelka van Houten
Jelka van Houten is a Dutch actress and singer known for her work in film, television, and theater, and as the younger sister of actress Carice van Houten.
-
D.
Lida Scholten
Lida Scholten is a Dutch art patron and co-founder of the Museum Beelden aan Zee in The Hague, known for her support of modern and contemporary sculpture.
-
E.
Sabina Beekman
Sabina Beekman is the wife of American publishing executive and former presidential candidate Steve Forbes.
- 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: Saskia van Houten Triple: [Goede tijden, slechte tijden, hasCharacter, Saskia van Houten]
Generated description
Saskia van Houten is a fictional character from the long-running Dutch soap opera "Goede tijden, slechte tijden."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Saskia van Houten Target entity description: Saskia van Houten is a fictional character from the long-running Dutch soap opera "Goede tijden, slechte tijden."
-
A.
Julia van der Toorn
Julia van der Toorn is a Dutch singer who gained national fame after winning the talent show The Voice of Holland.
-
B.
Astrid Nienhuis
Astrid Nienhuis is a Dutch politician who serves as the mayor of the municipality of Heemstede in the Netherlands.
-
C.
Jelka van Houten
Jelka van Houten is a Dutch actress and singer known for her work in film, television, and theater, and as the younger sister of actress Carice van Houten.
-
D.
Lida Scholten
Lida Scholten is a Dutch art patron and co-founder of the Museum Beelden aan Zee in The Hague, known for her support of modern and contemporary sculpture.
-
E.
Sabina Beekman
Sabina Beekman is the wife of American publishing executive and former presidential candidate Steve Forbes.
- 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_69e11e2c814c8190837d072789000486 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f1270f77fc8190aadcc02760d65ac0 |
completed | April 28, 2026, 9:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a809cc7fc81908c9d7164ed135b41 |
completed | May 18, 2026, 2:59 a.m. |
| NEDg | Description generation | batch_6a0a8484d02c8190a030f896a697820e |
completed | May 18, 2026, 3:16 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0a8538ae648190b1c6315a7af34d3f |
completed | May 18, 2026, 3:19 a.m. |
Created at: April 16, 2026, 8:17 p.m.