Triple
T7092701
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hasselt |
E165235
|
entity |
| Predicate | hasMayor |
P185
|
FINISHED |
| Object |
Steven Vandeput
Steven Vandeput is a Belgian politician who has served as the mayor of Hasselt and is known for his role in national and local government.
|
E642681
|
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: Steven Vandeput | Statement: [Hasselt, hasMayor, Steven Vandeput]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Steven Vandeput Context triple: [Hasselt, hasMayor, Steven Vandeput]
-
A.
Peter De Vries
Peter De Vries was an American novelist and humorist known for his witty, satirical fiction and contributions to The New Yorker.
-
B.
Tom Veldkamp
Tom Veldkamp is a Dutch academic and professor who serves as rector magnificus (chief academic officer) of the University of Twente.
-
C.
Dirk Westervelt
Dirk Westervelt is a film editor known for his work on major feature films, including the crime drama "Notorious."
-
D.
James Heerdegen
James Heerdegen is an American camera technician and dolly grip best known to the public for his former marriage to actress Christina Ricci.
-
E.
Christian Huitema
Christian Huitema is a French computer scientist and Internet pioneer known for his influential work on networking protocols and IPv6 transition technologies.
- 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: Steven Vandeput Triple: [Hasselt, hasMayor, Steven Vandeput]
Generated description
Steven Vandeput is a Belgian politician who has served as the mayor of Hasselt and is known for his role in national and local government.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Steven Vandeput Target entity description: Steven Vandeput is a Belgian politician who has served as the mayor of Hasselt and is known for his role in national and local government.
-
A.
Peter De Vries
Peter De Vries was an American novelist and humorist known for his witty, satirical fiction and contributions to The New Yorker.
-
B.
Tom Veldkamp
Tom Veldkamp is a Dutch academic and professor who serves as rector magnificus (chief academic officer) of the University of Twente.
-
C.
Dirk Westervelt
Dirk Westervelt is a film editor known for his work on major feature films, including the crime drama "Notorious."
-
D.
James Heerdegen
James Heerdegen is an American camera technician and dolly grip best known to the public for his former marriage to actress Christina Ricci.
-
E.
Christian Huitema
Christian Huitema is a French computer scientist and Internet pioneer known for his influential work on networking protocols and IPv6 transition technologies.
- 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_69c6887e8c10819091cee237560d32da |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e532513c8190968eea8a0d3235a0 |
completed | March 27, 2026, 8:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c79c960484819098228cebccb8c935 |
completed | March 28, 2026, 9:17 a.m. |
| NEDg | Description generation | batch_69c79ef0e8448190a8f0a7572ee87f5f |
completed | March 28, 2026, 9:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c79fa4e69881909cf991a5d0ab3de9 |
completed | March 28, 2026, 9:30 a.m. |
Created at: March 27, 2026, 2:41 p.m.