Triple
T5893306
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Best |
E131041
|
entity |
| Predicate | hasMayor |
P185
|
FINISHED |
| Object |
Theo Weterings
Theo Weterings is a Dutch politician who serves as the mayor of the municipality of Best in the Netherlands.
|
E554093
|
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: Theo Weterings | Statement: [Best, hasMayor, Theo Weterings]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Theo Weterings Context triple: [Best, hasMayor, Theo Weterings]
-
A.
Jos Wienen
Jos Wienen is a Dutch politician who serves as the mayor of the city of Haarlem in the Netherlands.
-
B.
Peter Noorwits
Peter Noorwits was a Dutch architect known for his work on prominent ecclesiastical buildings in the Netherlands, including the Nieuwe Kerk in The Hague.
-
C.
Chris de Weijer
Chris de Weijer is a Dutch architect best known as one of the founding partners of the internationally renowned architecture firm Mecanoo.
-
D.
Christian Huitema
Christian Huitema is a French computer scientist and Internet pioneer known for his influential work on networking protocols and IPv6 transition technologies.
-
E.
Peter Welinder
Peter Welinder is a computer scientist and entrepreneur known for his contributions to deep reinforcement learning and for co-authoring the Hindsight Experience Replay technique.
- 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: Theo Weterings Triple: [Best, hasMayor, Theo Weterings]
Generated description
Theo Weterings is a Dutch politician who serves as the mayor of the municipality of Best in the Netherlands.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Theo Weterings Target entity description: Theo Weterings is a Dutch politician who serves as the mayor of the municipality of Best in the Netherlands.
-
A.
Jos Wienen
Jos Wienen is a Dutch politician who serves as the mayor of the city of Haarlem in the Netherlands.
-
B.
Peter Noorwits
Peter Noorwits was a Dutch architect known for his work on prominent ecclesiastical buildings in the Netherlands, including the Nieuwe Kerk in The Hague.
-
C.
Chris de Weijer
Chris de Weijer is a Dutch architect best known as one of the founding partners of the internationally renowned architecture firm Mecanoo.
-
D.
Christian Huitema
Christian Huitema is a French computer scientist and Internet pioneer known for his influential work on networking protocols and IPv6 transition technologies.
-
E.
Peter Welinder
Peter Welinder is a computer scientist and entrepreneur known for his contributions to deep reinforcement learning and for co-authoring the Hindsight Experience Replay technique.
- 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_69c00857439c819095950754176aa58a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c036b5c68481909fdcba428238c74d |
completed | March 22, 2026, 6:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0b15146888190ab86eaf9e565ee28 |
completed | March 23, 2026, 3:19 a.m. |
| NEDg | Description generation | batch_69c0b53ad6dc8190927653c470515963 |
completed | March 23, 2026, 3:36 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c0b5de67208190b1dda9509767c2a7 |
completed | March 23, 2026, 3:39 a.m. |
Created at: March 22, 2026, 3:58 p.m.