Triple
T23479301
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hugo Brandt Corstius |
E570357
|
entity |
| Predicate | pseudonym |
P39
|
FINISHED |
| Object |
Victor Baarn
Victor Baarn is a pseudonym used by Dutch writer, linguist, and columnist Hugo Brandt Corstius.
|
E1588435
|
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: Victor Baarn | Statement: [Hugo Brandt Corstius, pseudonym, Victor Baarn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Victor Baarn Context triple: [Hugo Brandt Corstius, pseudonym, Victor Baarn]
-
A.
Victor Francen
Victor Francen was a Belgian-born French actor known for his distinguished presence in European and Hollywood films of the 1930s and 1940s.
-
B.
Hans Hoogervorst
Hans Hoogervorst is a Dutch politician and financial regulator known for his leadership in international accounting standard-setting and financial oversight.
-
C.
Hans Bonte
Hans Bonte is a Belgian politician known for serving as the mayor of Vilvoorde and as a member of the federal parliament.
-
D.
Bernard Baars
Bernard Baars is a cognitive scientist best known for developing global workspace theory, a prominent model of human consciousness.
-
E.
Marius de Vries
Marius de Vries is a British composer, producer, and arranger known for his innovative work on film soundtracks and collaborations with prominent pop and electronic artists.
- 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: Victor Baarn Triple: [Hugo Brandt Corstius, pseudonym, Victor Baarn]
Generated description
Victor Baarn is a pseudonym used by Dutch writer, linguist, and columnist Hugo Brandt Corstius.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Victor Baarn Target entity description: Victor Baarn is a pseudonym used by Dutch writer, linguist, and columnist Hugo Brandt Corstius.
-
A.
Victor Francen
Victor Francen was a Belgian-born French actor known for his distinguished presence in European and Hollywood films of the 1930s and 1940s.
-
B.
Hans Hoogervorst
Hans Hoogervorst is a Dutch politician and financial regulator known for his leadership in international accounting standard-setting and financial oversight.
-
C.
Hans Bonte
Hans Bonte is a Belgian politician known for serving as the mayor of Vilvoorde and as a member of the federal parliament.
-
D.
Bernard Baars
Bernard Baars is a cognitive scientist best known for developing global workspace theory, a prominent model of human consciousness.
-
E.
Marius de Vries
Marius de Vries is a British composer, producer, and arranger known for his innovative work on film soundtracks and collaborations with prominent pop and electronic artists.
- 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_69e245af8a88819084f2704f6d265a92 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1a74f48d8819080e875aaea8b46b3 |
completed | April 29, 2026, 6:38 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c8263ec988190b4f86f4bc283d929 |
completed | May 19, 2026, 3:31 p.m. |
| NEDg | Description generation | batch_6a0ca6f06a348190bfdba0452077f45b |
completed | May 19, 2026, 6:07 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0ca7c5176c81908221307bb50ff4ed |
completed | May 19, 2026, 6:11 p.m. |
Created at: April 17, 2026, 6:02 p.m.