Triple
T1347040
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haarlem |
E28594
|
entity |
| Predicate | hasMayor |
P185
|
FINISHED |
| Object |
Jos Wienen
Jos Wienen is a Dutch politician who serves as the mayor of the city of Haarlem in the Netherlands.
|
E177414
|
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: Jos Wienen | Statement: [Haarlem, hasMayor, Jos Wienen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jos Wienen Context triple: [Haarlem, hasMayor, Jos Wienen]
-
A.
Christian Huitema
Christian Huitema is a French computer scientist and Internet pioneer known for his influential work on networking protocols and IPv6 transition technologies.
-
B.
Hans Bonte
Hans Bonte is a Belgian politician known for serving as the mayor of Vilvoorde and as a member of the federal parliament.
-
C.
Theo de Meester
Theo de Meester was a Dutch liberal politician who served as Prime Minister of the Netherlands in the early 20th century.
-
D.
Sjoerd Soeters
Sjoerd Soeters is a Dutch architect known for his postmodern, human-scaled urban designs and influential waterfront redevelopment projects in the Netherlands.
-
E.
Leo Geurts
Leo Geurts was a Dutch computer scientist known for co-developing the ABC programming language, an influential precursor to Python.
- 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: Jos Wienen Triple: [Haarlem, hasMayor, Jos Wienen]
Generated description
Jos Wienen is a Dutch politician who serves as the mayor of the city of Haarlem in the Netherlands.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jos Wienen Target entity description: Jos Wienen is a Dutch politician who serves as the mayor of the city of Haarlem in the Netherlands.
-
A.
Christian Huitema
Christian Huitema is a French computer scientist and Internet pioneer known for his influential work on networking protocols and IPv6 transition technologies.
-
B.
Hans Bonte
Hans Bonte is a Belgian politician known for serving as the mayor of Vilvoorde and as a member of the federal parliament.
-
C.
Theo de Meester
Theo de Meester was a Dutch liberal politician who served as Prime Minister of the Netherlands in the early 20th century.
-
D.
Sjoerd Soeters
Sjoerd Soeters is a Dutch architect known for his postmodern, human-scaled urban designs and influential waterfront redevelopment projects in the Netherlands.
-
E.
Leo Geurts
Leo Geurts was a Dutch computer scientist known for co-developing the ABC programming language, an influential precursor to Python.
- 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_69a49854eb3481908c7d56b2e449a290 |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c2406c488190b2c04d54d9c5e94c |
completed | March 1, 2026, 10:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad36f900848190bb00cd7ad8eada60 |
completed | March 8, 2026, 8:44 a.m. |
| NEDg | Description generation | batch_69ad38706fcc81909e42a6a713605237 |
completed | March 8, 2026, 8:50 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad38c50fd8819087cce60b83017766 |
completed | March 8, 2026, 8:52 a.m. |
Created at: March 1, 2026, 7:56 p.m.