Triple
T15083002
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moerdijk |
E360196
|
entity |
| Predicate | containsSettlement |
P847
|
FINISHED |
| Object |
Klundert
Klundert is a small historic town in the Dutch province of North Brabant, known for its former fortifications and traditional architecture.
|
E1137036
|
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: Klundert | Statement: [Moerdijk, containsSettlement, Klundert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Klundert Context triple: [Moerdijk, containsSettlement, Klundert]
-
A.
Kolderbos
Kolderbos is a residential district of the Belgian city of Genk, known for its post-war social housing and multicultural community.
-
B.
Koggenland
Koggenland is a rural municipality in the province of North Holland in the Netherlands, known for its agricultural landscape and historic villages.
-
C.
Klütz
Klütz is a small historic town in northern Germany’s Mecklenburg-Vorpommern region, known for its proximity to the Baltic Sea and the baroque Bothmer Castle.
-
D.
Dahlkemper
Dahlkemper is a surname most prominently associated with American professional soccer player Abby Dahlkemper.
-
E.
Hellerud
Hellerud is a residential neighborhood in Oslo, Norway, known for its hillside location and proximity to green areas and public transport.
- 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: Klundert Triple: [Moerdijk, containsSettlement, Klundert]
Generated description
Klundert is a small historic town in the Dutch province of North Brabant, known for its former fortifications and traditional architecture.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Klundert Target entity description: Klundert is a small historic town in the Dutch province of North Brabant, known for its former fortifications and traditional architecture.
-
A.
Kolderbos
Kolderbos is a residential district of the Belgian city of Genk, known for its post-war social housing and multicultural community.
-
B.
Koggenland
Koggenland is a rural municipality in the province of North Holland in the Netherlands, known for its agricultural landscape and historic villages.
-
C.
Klütz
Klütz is a small historic town in northern Germany’s Mecklenburg-Vorpommern region, known for its proximity to the Baltic Sea and the baroque Bothmer Castle.
-
D.
Dahlkemper
Dahlkemper is a surname most prominently associated with American professional soccer player Abby Dahlkemper.
-
E.
Hellerud
Hellerud is a residential neighborhood in Oslo, Norway, known for its hillside location and proximity to green areas and public transport.
- 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_69d85a035aa88190b52a139d3a1b7b6d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0027450a48190a84588b6aaf84ebf |
completed | April 15, 2026, 9:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feae179a24819097019976707c93e1 |
completed | May 9, 2026, 3:46 a.m. |
| NEDg | Description generation | batch_69feb074bc9c8190a080bb57bb1844be |
completed | May 9, 2026, 3:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69feb0c2af8081909e845bb8c9f9cdc7 |
completed | May 9, 2026, 3:57 a.m. |
Created at: April 10, 2026, 3:03 a.m.