Triple
T7667997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | de Jonge |
E173672
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Peter de Jonge
Peter de Jonge is an American author and journalist best known for co-writing several bestselling crime novels with James Patterson.
|
E689571
|
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: Peter de Jonge | Statement: [de Jonge, hasNotableBearer, Peter de Jonge]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peter de Jonge Context triple: [de Jonge, hasNotableBearer, Peter de Jonge]
-
A.
Piet de Jong
Piet de Jong was a Dutch politician who served as Prime Minister of the Netherlands from 1967 to 1971.
-
B.
Johan de Jonge
Johan de Jonge is a person notable enough to be recognized as a distinguished bearer of the surname "de Jonge."
-
C.
Pieter R. de Jong
Pieter R. de Jong is a Dutch professional who studied at Utrecht University and is recognized as a notable alumnus for his contributions in his field.
-
D.
Barend Biesheuvel
Barend Biesheuvel was a Dutch politician of the Anti-Revolutionary Party who served as Prime Minister of the Netherlands from 1971 to 1973.
-
E.
Hans van Heeswijk
Hans van Heeswijk is a Dutch architect known for designing prominent cultural buildings, including major expansions and renovations of museums in the Netherlands.
- 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: Peter de Jonge Triple: [de Jonge, hasNotableBearer, Peter de Jonge]
Generated description
Peter de Jonge is an American author and journalist best known for co-writing several bestselling crime novels with James Patterson.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Peter de Jonge Target entity description: Peter de Jonge is an American author and journalist best known for co-writing several bestselling crime novels with James Patterson.
-
A.
Piet de Jong
Piet de Jong was a Dutch politician who served as Prime Minister of the Netherlands from 1967 to 1971.
-
B.
Johan de Jonge
Johan de Jonge is a person notable enough to be recognized as a distinguished bearer of the surname "de Jonge."
-
C.
Pieter R. de Jong
Pieter R. de Jong is a Dutch professional who studied at Utrecht University and is recognized as a notable alumnus for his contributions in his field.
-
D.
Barend Biesheuvel
Barend Biesheuvel was a Dutch politician of the Anti-Revolutionary Party who served as Prime Minister of the Netherlands from 1971 to 1973.
-
E.
Hans van Heeswijk
Hans van Heeswijk is a Dutch architect known for designing prominent cultural buildings, including major expansions and renovations of museums in the Netherlands.
- 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_69c699562484819086752091e3164a27 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c701c26ffc8190894fdef92f877f38 |
completed | March 27, 2026, 10:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c902af947081908d09cba5e4e4e434 |
completed | March 29, 2026, 10:45 a.m. |
| NEDg | Description generation | batch_69c9037a4d1c8190a569a1e64ccc6d45 |
completed | March 29, 2026, 10:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c903d3b75c81909658b730c6572aed |
completed | March 29, 2026, 10:49 a.m. |
Created at: March 27, 2026, 4 p.m.