Triple
T4283883
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Molenlanden |
E97218
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object |
Goudriaan
Goudriaan is a small village in the Dutch province of South Holland, known for its rural character and historic polder landscape.
|
E440392
|
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: Goudriaan | Statement: [Molenlanden, hasSettlement, Goudriaan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Goudriaan Context triple: [Molenlanden, hasSettlement, Goudriaan]
-
A.
Van der Madeweg
Van der Madeweg is a metro station in Amsterdam that serves as a stop on the city's rapid transit network.
-
B.
Hendrik de Vries
Hendrik de Vries was a mathematician who supervised and mentored the influential algebraist Bartel Leendert van der Waerden.
-
C.
Dirk Jan de Geer
Dirk Jan de Geer was a Dutch politician who served twice as Prime Minister of the Netherlands, most notably during the early years of World War II.
-
D.
Leendert Bramer
Leendert Bramer was a Dutch Golden Age painter known for his atmospheric, often nocturnal scenes and frescoes influenced by Italian art.
-
E.
Bep Voskuijl
Bep Voskuijl was a Dutch office worker who helped hide Anne Frank and her family during World War II by providing food, supplies, and support to those in the Secret Annex.
- 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: Goudriaan Triple: [Molenlanden, hasSettlement, Goudriaan]
Generated description
Goudriaan is a small village in the Dutch province of South Holland, known for its rural character and historic polder landscape.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Goudriaan Target entity description: Goudriaan is a small village in the Dutch province of South Holland, known for its rural character and historic polder landscape.
-
A.
Van der Madeweg
Van der Madeweg is a metro station in Amsterdam that serves as a stop on the city's rapid transit network.
-
B.
Hendrik de Vries
Hendrik de Vries was a mathematician who supervised and mentored the influential algebraist Bartel Leendert van der Waerden.
-
C.
Dirk Jan de Geer
Dirk Jan de Geer was a Dutch politician who served twice as Prime Minister of the Netherlands, most notably during the early years of World War II.
-
D.
Leendert Bramer
Leendert Bramer was a Dutch Golden Age painter known for his atmospheric, often nocturnal scenes and frescoes influenced by Italian art.
-
E.
Bep Voskuijl
Bep Voskuijl was a Dutch office worker who helped hide Anne Frank and her family during World War II by providing food, supplies, and support to those in the Secret Annex.
- 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_69b3454595848190a0e6bbb6a2bea040 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3503c062c81908f9a9eeab5381ec9 |
completed | March 12, 2026, 11:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b613464adc8190bc82f599d30d7b13 |
completed | March 15, 2026, 2:02 a.m. |
| NEDg | Description generation | batch_69b613f0d5dc8190a48e60c63ea9d717 |
completed | March 15, 2026, 2:05 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b617f70f608190bce1043c2254c9e5 |
completed | March 15, 2026, 2:22 a.m. |
Created at: March 12, 2026, 11:07 p.m.