Triple
T4741055
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rutger Jan Schimmelpenninck |
E105241
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Rutger
Rutger is a masculine given name of Dutch origin, commonly used in the Netherlands and other Germanic-language regions.
|
E466655
|
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: Rutger | Statement: [Rutger Jan Schimmelpenninck, givenName, Rutger]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rutger Context triple: [Rutger Jan Schimmelpenninck, givenName, Rutger]
-
A.
Randolf
Randolf is a surname most notably associated with Danish-American silent film actor Anders Randolf.
-
B.
Furthman
Furthman is a surname most notably associated with American screenwriter Jules Furthman, known for his work on classic Hollywood films.
-
C.
Rudi
Rudi is a common diminutive form of the given name Rudolf, used in various European languages.
-
D.
Tucker
Tucker is a surname most notably associated with Albert W. Tucker, a Canadian-American mathematician and game theorist known for his contributions to topology and the formalization of the prisoner's dilemma.
-
E.
Regis
Regis is an honorific term historically used in English to denote royal association, particularly in place names granted royal patronage.
- 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: Rutger Triple: [Rutger Jan Schimmelpenninck, givenName, Rutger]
Generated description
Rutger is a masculine given name of Dutch origin, commonly used in the Netherlands and other Germanic-language regions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rutger Target entity description: Rutger is a masculine given name of Dutch origin, commonly used in the Netherlands and other Germanic-language regions.
-
A.
Randolf
Randolf is a surname most notably associated with Danish-American silent film actor Anders Randolf.
-
B.
Furthman
Furthman is a surname most notably associated with American screenwriter Jules Furthman, known for his work on classic Hollywood films.
-
C.
Rudi
Rudi is a common diminutive form of the given name Rudolf, used in various European languages.
-
D.
Tucker
Tucker is a surname most notably associated with Albert W. Tucker, a Canadian-American mathematician and game theorist known for his contributions to topology and the formalization of the prisoner's dilemma.
-
E.
Regis
Regis is an honorific term historically used in English to denote royal association, particularly in place names granted royal patronage.
- 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_69bd43ef87a48190a5bc3600711aa032 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd64a5f3548190a6acf1dcfd64d11d |
completed | March 20, 2026, 3:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be3a28ca648190a44d178826926812 |
completed | March 21, 2026, 6:26 a.m. |
| NEDg | Description generation | batch_69be3c443c6881908d1a3de22ff1380b |
completed | March 21, 2026, 6:35 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be3d0aa1d48190a2af91d251cb5561 |
completed | March 21, 2026, 6:39 a.m. |
Created at: March 20, 2026, 1:19 p.m.