Triple
T15460165
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Film School of Denmark |
E371875
|
entity |
| Predicate | hasAlumnus |
P51
|
FINISHED |
| Object |
Henrik Ruben Genz
Henrik Ruben Genz is a Danish film director and screenwriter known for works such as "Terribly Happy" and "Chinaman."
|
E1158770
|
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 Ruben Genz | Statement: [National Film School of Denmark, hasAlumnus, Henrik Ruben Genz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Henrik Ruben Genz Context triple: [National Film School of Denmark, hasAlumnus, Henrik Ruben Genz]
-
A.
Aurskog-Høland
Aurskog-Høland is a rural municipality in Viken county, Norway, known for its forests, agriculture, and scattered villages east of Oslo.
-
B.
Kevin Haaland
Kevin Haaland is a guitarist best known for being an early member of the Christian rock band Skillet.
-
C.
Torbjørn Bergerud
Torbjørn Bergerud is a Norwegian professional handball goalkeeper known for his key role on the national team and strong performances in international competitions.
-
D.
Fabian Geyer
Fabian Geyer is a German local politician who serves as the mayor of the northern city of Flensburg.
-
E.
Edward Warschilka
Edward Warschilka was a film editor best known for his work on the 1973 mystery thriller "The Last of Sheila."
- 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 Ruben Genz Triple: [National Film School of Denmark, hasAlumnus, Henrik Ruben Genz]
Generated description
Henrik Ruben Genz is a Danish film director and screenwriter known for works such as "Terribly Happy" and "Chinaman."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Henrik Ruben Genz Target entity description: Henrik Ruben Genz is a Danish film director and screenwriter known for works such as "Terribly Happy" and "Chinaman."
-
A.
Aurskog-Høland
Aurskog-Høland is a rural municipality in Viken county, Norway, known for its forests, agriculture, and scattered villages east of Oslo.
-
B.
Kevin Haaland
Kevin Haaland is a guitarist best known for being an early member of the Christian rock band Skillet.
-
C.
Torbjørn Bergerud
Torbjørn Bergerud is a Norwegian professional handball goalkeeper known for his key role on the national team and strong performances in international competitions.
-
D.
Fabian Geyer
Fabian Geyer is a German local politician who serves as the mayor of the northern city of Flensburg.
-
E.
Edward Warschilka
Edward Warschilka was a film editor best known for his work on the 1973 mystery thriller "The Last of Sheila."
- 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_69d85cc8bd308190886949510b42e764 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f17663c8190b995c7c3129c90d6 |
completed | April 16, 2026, 1:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff2cfd76cc8190b3d8148ffe872887 |
completed | May 9, 2026, 12:47 p.m. |
| NEDg | Description generation | batch_69ff2ead88b0819093046c5f0dae8674 |
completed | May 9, 2026, 12:55 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff2f4a404c81909a391d3d2cba1ee8 |
completed | May 9, 2026, 12:57 p.m. |
Created at: April 10, 2026, 3:32 a.m.