Triple
T14723722
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Johannes van Rossum |
E345879
|
entity |
| Predicate | hasFamilyName |
P18
|
FINISHED |
| Object | van Rossum |
E345879
|
NE FINISHED |
How this triple was built (2 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: van Rossum | Statement: [Johannes van Rossum, hasFamilyName, van Rossum]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: van Rossum Context triple: [Johannes van Rossum, hasFamilyName, van Rossum]
-
A.
Johannes van Rossum
chosen
Johannes van Rossum was a Dutch coachman and later companion closely associated with Princess Marianne of the Netherlands, with whom he had a long-term, controversial relationship.
-
B.
Guido van Rossum
Guido van Rossum is a Dutch programmer best known as the creator of the Python programming language.
-
C.
Rossum
Rossum is the surname of Emmy Rossum, an American actress and singer best known for her role as Fiona Gallagher on the television series "Shameless."
-
D.
Lambert Meertens
Lambert Meertens is a Dutch computer scientist known for his influential work in programming language design and formal methods.
-
E.
Robert Kern
Robert Kern was an American film editor active during Hollywood’s classic studio era, known for his work on numerous prominent MGM productions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d822e5911c8190ba589f957dbd9ba7 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec25e9a14819081fa06fc601f295d |
completed | April 14, 2026, 10:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdf09791e081908a1262717fd31445 |
completed | May 8, 2026, 2:17 p.m. |
Created at: April 10, 2026, 1:29 a.m.