Triple
T11012865
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amersfoort |
E260285
|
entity |
| Predicate | hasLandmark |
P105
|
FINISHED |
| Object |
Muurhuizen
Muurhuizen is a historic street in Amersfoort, Netherlands, lined with medieval houses built along the former city wall.
|
E901990
|
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: Muurhuizen | Statement: [Amersfoort, hasLandmark, Muurhuizen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Muurhuizen Context triple: [Amersfoort, hasLandmark, Muurhuizen]
-
A.
Zevenhuizen
Zevenhuizen is a village in the Dutch province of Groningen, located within the municipality of Westerkwartier.
-
B.
Biezenmortel
Biezenmortel is a small village in the Dutch province of North Brabant, known for its rural character and proximity to larger towns like Tilburg and ’s-Hertogenbosch.
-
C.
Moosseedorf
Moosseedorf is a municipality in the canton of Bern in Switzerland, known for its proximity to the city of Bern and the nearby Moossee lake.
-
D.
Veenhuizen
Veenhuizen is a historic Dutch village in the province of Drenthe, best known for its former penal colonies and unique heritage as a 19th-century social reform experiment.
-
E.
Nijverdal
Nijverdal is a town in the Dutch province of Overijssel known as a gateway to the Sallandse Heuvelrug National Park and its surrounding natural landscapes.
- 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: Muurhuizen Triple: [Amersfoort, hasLandmark, Muurhuizen]
Generated description
Muurhuizen is a historic street in Amersfoort, Netherlands, lined with medieval houses built along the former city wall.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Muurhuizen Target entity description: Muurhuizen is a historic street in Amersfoort, Netherlands, lined with medieval houses built along the former city wall.
-
A.
Zevenhuizen
Zevenhuizen is a village in the Dutch province of Groningen, located within the municipality of Westerkwartier.
-
B.
Biezenmortel
Biezenmortel is a small village in the Dutch province of North Brabant, known for its rural character and proximity to larger towns like Tilburg and ’s-Hertogenbosch.
-
C.
Moosseedorf
Moosseedorf is a municipality in the canton of Bern in Switzerland, known for its proximity to the city of Bern and the nearby Moossee lake.
-
D.
Veenhuizen
Veenhuizen is a historic Dutch village in the province of Drenthe, best known for its former penal colonies and unique heritage as a 19th-century social reform experiment.
-
E.
Nijverdal
Nijverdal is a town in the Dutch province of Overijssel known as a gateway to the Sallandse Heuvelrug National Park and its surrounding natural landscapes.
- 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_69d6aa9687448190b28d353b1b6a610e |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7978b1e888190b297f107f6021b59 |
completed | April 9, 2026, 12:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3a98725808190903639866a3e745f |
completed | April 18, 2026, 3:55 p.m. |
| NEDg | Description generation | batch_69e3abe492388190a2f5752f6bad1220 |
completed | April 18, 2026, 4:05 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e3b1efe4a88190884eb5186954cf39 |
completed | April 18, 2026, 4:31 p.m. |
Created at: April 8, 2026, 9:25 p.m.