Triple
T18277445
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Emily de Jongh-Elhage |
E437774
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
de Jongh-Elhage
de Jongh-Elhage is a Dutch-origin surname most notably associated with Curaçaoan politician and former Prime Minister Emily de Jongh-Elhage.
|
E1315234
|
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: de Jongh-Elhage | Statement: [Emily de Jongh-Elhage, familyName, de Jongh-Elhage]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: de Jongh-Elhage Context triple: [Emily de Jongh-Elhage, familyName, de Jongh-Elhage]
-
A.
de Jonge
De Jonge is a Dutch surname borne by various notable figures, including politicians, artists, and athletes in the Netherlands.
-
B.
de Haan
de Haan is a Dutch surname borne by various notable figures in the Netherlands and beyond.
-
C.
Evert van Benthem
Evert van Benthem is a Dutch former speed skater best known for winning the Elfstedentocht (Eleven Cities Tour) twice, in 1985 and 1986.
-
D.
Jongerius
Jongerius is a Dutch surname most notably associated with Hella Jongerius, a renowned contemporary industrial and product designer.
-
E.
Lex de Bruijn
Lex de Bruijn is an actor known for his role in Jean-Luc Godard’s 1967 French New Wave film "La Chinoise."
- 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: de Jongh-Elhage Triple: [Emily de Jongh-Elhage, familyName, de Jongh-Elhage]
Generated description
de Jongh-Elhage is a Dutch-origin surname most notably associated with Curaçaoan politician and former Prime Minister Emily de Jongh-Elhage.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: de Jongh-Elhage Target entity description: de Jongh-Elhage is a Dutch-origin surname most notably associated with Curaçaoan politician and former Prime Minister Emily de Jongh-Elhage.
-
A.
de Jonge
De Jonge is a Dutch surname borne by various notable figures, including politicians, artists, and athletes in the Netherlands.
-
B.
de Haan
de Haan is a Dutch surname borne by various notable figures in the Netherlands and beyond.
-
C.
Evert van Benthem
Evert van Benthem is a Dutch former speed skater best known for winning the Elfstedentocht (Eleven Cities Tour) twice, in 1985 and 1986.
-
D.
Jongerius
Jongerius is a Dutch surname most notably associated with Hella Jongerius, a renowned contemporary industrial and product designer.
-
E.
Lex de Bruijn
Lex de Bruijn is an actor known for his role in Jean-Luc Godard’s 1967 French New Wave film "La Chinoise."
- 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_69d8b914530c8190b4474d862a2b2a1b |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e500528bb88190a9f9ba6428cc2076 |
completed | April 19, 2026, 4:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a03b3fdff648190a738adf86d8d470b |
completed | May 12, 2026, 11:13 p.m. |
| NEDg | Description generation | batch_6a03b4bb6ae48190a68c2a3314f0d3ba |
completed | May 12, 2026, 11:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a03b56958a0819088613a9726c71819 |
completed | May 12, 2026, 11:19 p.m. |
Created at: April 10, 2026, 10:34 a.m.